{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResnetTrick_s128_e80\n",
    "\n",
    "> size 128 bs 64 80 epochs runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# setup and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/ayasyrev/model_constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/kornia/kornia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kornia.contrib import MaxBlurPool2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.basic_train import *\n",
    "from fastai.vision import *\n",
    "from fastai.script import *\n",
    "from model_constructor.net import *\n",
    "from model_constructor.layers import SimpleSelfAttention, ConvLayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ranger deep learning optimizer - RAdam + Lookahead combined.\n",
    "  #https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer\n",
    "\n",
    "  #Ranger has now been used to capture 12 records on the FastAI leaderboard.\n",
    "\n",
    "  #This version = 9.3.19  \n",
    "\n",
    "  #Credits:\n",
    "  #RAdam -->  https://github.com/LiyuanLucasLiu/RAdam\n",
    "  #Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.\n",
    "  #Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610\n",
    "\n",
    "  #summary of changes: \n",
    "  #full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights), \n",
    "  #supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.\n",
    "  #changes 8/31/19 - fix references to *self*.N_sma_threshold; \n",
    "                  #changed eps to 1e-5 as better default than 1e-8.\n",
    "\n",
    "import math\n",
    "import torch\n",
    "from torch.optim.optimizer import Optimizer, required\n",
    "import itertools as it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mish(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        print(\"Mish activation loaded...\")\n",
    "\n",
    "    def forward(self, x):  \n",
    "        #save 1 second per epoch with no x= x*() and then return x...just inline it.\n",
    "        return x *( torch.tanh(F.softplus(x))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Ranger(Optimizer):\n",
    "\n",
    "    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95,0.999), eps=1e-5, weight_decay=0):\n",
    "        #parameter checks\n",
    "        if not 0.0 <= alpha <= 1.0:\n",
    "            raise ValueError(f'Invalid slow update rate: {alpha}')\n",
    "        if not 1 <= k:\n",
    "            raise ValueError(f'Invalid lookahead steps: {k}')\n",
    "        if not lr > 0:\n",
    "            raise ValueError(f'Invalid Learning Rate: {lr}')\n",
    "        if not eps > 0:\n",
    "            raise ValueError(f'Invalid eps: {eps}')\n",
    "\n",
    "        #parameter comments:\n",
    "        # beta1 (momentum) of .95 seems to work better than .90...\n",
    "        #N_sma_threshold of 5 seems better in testing than 4.\n",
    "        #In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.\n",
    "\n",
    "        #prep defaults and init torch.optim base\n",
    "        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay)\n",
    "        super().__init__(params,defaults)\n",
    "\n",
    "        #adjustable threshold\n",
    "        self.N_sma_threshhold = N_sma_threshhold\n",
    "\n",
    "        #now we can get to work...\n",
    "        #removed as we now use step from RAdam...no need for duplicate step counting\n",
    "        #for group in self.param_groups:\n",
    "        #    group[\"step_counter\"] = 0\n",
    "            #print(\"group step counter init\")\n",
    "\n",
    "        #look ahead params\n",
    "        self.alpha = alpha\n",
    "        self.k = k \n",
    "\n",
    "        #radam buffer for state\n",
    "        self.radam_buffer = [[None,None,None] for ind in range(10)]\n",
    "\n",
    "        #self.first_run_check=0\n",
    "\n",
    "        #lookahead weights\n",
    "        #9/2/19 - lookahead param tensors have been moved to state storage.  \n",
    "        #This should resolve issues with load/save where weights were left in GPU memory from first load, slowing down future runs.\n",
    "\n",
    "        #self.slow_weights = [[p.clone().detach() for p in group['params']]\n",
    "        #                     for group in self.param_groups]\n",
    "\n",
    "        #don't use grad for lookahead weights\n",
    "        #for w in it.chain(*self.slow_weights):\n",
    "        #    w.requires_grad = False\n",
    "\n",
    "    def __setstate__(self, state):\n",
    "        print(\"set state called\")\n",
    "        super(Ranger, self).__setstate__(state)\n",
    "\n",
    "\n",
    "    def step(self, closure=None):\n",
    "        loss = None\n",
    "        #note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.  \n",
    "        #Uncomment if you need to use the actual closure...\n",
    "\n",
    "        #if closure is not None:\n",
    "            #loss = closure()\n",
    "\n",
    "        #Evaluate averages and grad, update param tensors\n",
    "        for group in self.param_groups:\n",
    "\n",
    "            for p in group['params']:\n",
    "                if p.grad is None:\n",
    "                    continue\n",
    "                grad = p.grad.data.float()\n",
    "                if grad.is_sparse:\n",
    "                    raise RuntimeError('Ranger optimizer does not support sparse gradients')\n",
    "\n",
    "                p_data_fp32 = p.data.float()\n",
    "\n",
    "                state = self.state[p]  #get state dict for this param\n",
    "\n",
    "                if len(state) == 0:   #if first time to run...init dictionary with our desired entries\n",
    "                    #if self.first_run_check==0:\n",
    "                        #self.first_run_check=1\n",
    "                        #print(\"Initializing slow buffer...should not see this at load from saved model!\")\n",
    "                    state['step'] = 0\n",
    "                    state['exp_avg'] = torch.zeros_like(p_data_fp32)\n",
    "                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)\n",
    "\n",
    "                    #look ahead weight storage now in state dict \n",
    "                    state['slow_buffer'] = torch.empty_like(p.data)\n",
    "                    state['slow_buffer'].copy_(p.data)\n",
    "\n",
    "                else:\n",
    "                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)\n",
    "                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)\n",
    "\n",
    "                #begin computations \n",
    "                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n",
    "                beta1, beta2 = group['betas']\n",
    "\n",
    "                #compute variance mov avg\n",
    "                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n",
    "                #compute mean moving avg\n",
    "                exp_avg.mul_(beta1).add_(1 - beta1, grad)\n",
    "\n",
    "                state['step'] += 1\n",
    "\n",
    "\n",
    "                buffered = self.radam_buffer[int(state['step'] % 10)]\n",
    "                if state['step'] == buffered[0]:\n",
    "                    N_sma, step_size = buffered[1], buffered[2]\n",
    "                else:\n",
    "                    buffered[0] = state['step']\n",
    "                    beta2_t = beta2 ** state['step']\n",
    "                    N_sma_max = 2 / (1 - beta2) - 1\n",
    "                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)\n",
    "                    buffered[1] = N_sma\n",
    "                    if N_sma > self.N_sma_threshhold:\n",
    "                        step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])\n",
    "                    else:\n",
    "                        step_size = 1.0 / (1 - beta1 ** state['step'])\n",
    "                    buffered[2] = step_size\n",
    "\n",
    "                if group['weight_decay'] != 0:\n",
    "                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)\n",
    "\n",
    "                if N_sma > self.N_sma_threshhold:\n",
    "                    denom = exp_avg_sq.sqrt().add_(group['eps'])\n",
    "                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)\n",
    "                else:\n",
    "                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)\n",
    "\n",
    "                p.data.copy_(p_data_fp32)\n",
    "\n",
    "                #integrated look ahead...\n",
    "                #we do it at the param level instead of group level\n",
    "                if state['step'] % group['k'] == 0:\n",
    "                    slow_p = state['slow_buffer'] #get access to slow param tensor\n",
    "                    slow_p.add_(self.alpha, p.data - slow_p)  #(fast weights - slow weights) * alpha\n",
    "                    p.data.copy_(slow_p)  #copy interpolated weights to RAdam param tensor\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(size=128, woof=1, bs=64, workers=None, **kwargs):\n",
    "    if woof:\n",
    "        path = URLs.IMAGEWOOF    # if woof \n",
    "    else:\n",
    "        path = URLs.IMAGENETTE\n",
    "    path = untar_data(path)\n",
    "    print('data path  ', path)\n",
    "    n_gpus = num_distrib() or 1\n",
    "    if workers is None: workers = min(8, num_cpus()//n_gpus)\n",
    "    return (ImageList.from_folder(path).split_by_folder(valid='val')\n",
    "            .label_from_folder().transform(([flip_lr(p=0.5)], []), size=size)\n",
    "            .databunch(bs=bs, num_workers=workers)\n",
    "            .presize(size, scale=(0.35,1))\n",
    "            .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_learn(\n",
    "        gpu:Param(\"GPU to run on\", str)=None,\n",
    "        woof: Param(\"Use imagewoof (otherwise imagenette)\", int)=1,\n",
    "        size: Param(\"Size (px: 128,192,224)\", int)=128,\n",
    "        alpha: Param(\"Alpha\", float)=0.99, \n",
    "        mom: Param(\"Momentum\", float)=0.95, #? 0.9\n",
    "        eps: Param(\"epsilon\", float)=1e-6,\n",
    "        bs: Param(\"Batch size\", int)=64,\n",
    "        mixup: Param(\"Mixup\", float)=0.,\n",
    "        opt: Param(\"Optimizer (adam,rms,sgd)\", str)='ranger',\n",
    "        sa: Param(\"Self-attention\", int)=0,\n",
    "        sym: Param(\"Symmetry for self-attention\", int)=0,\n",
    "        model: Param('model as partial', callable) = xresnet50\n",
    "        ):\n",
    " \n",
    "    if   opt=='adam' : opt_func = partial(optim.Adam, betas=(mom,alpha), eps=eps)\n",
    "    elif opt=='ranger'  : opt_func = partial(Ranger,  betas=(mom,alpha), eps=eps)\n",
    "    data = get_data(size, woof, bs)\n",
    "    learn = (Learner(data, model(), wd=1e-2, opt_func=opt_func,\n",
    "             metrics=[accuracy,top_k_accuracy],\n",
    "             bn_wd=False, true_wd=True,\n",
    "             loss_func = LabelSmoothingCrossEntropy(),))\n",
    "    print('Learn path', learn.path)\n",
    "    if mixup: learn = learn.mixup(alpha=mixup)\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(learn):\n",
    "    learn.recorder.plot_losses()\n",
    "    learn.recorder.plot_metrics()\n",
    "    learn.recorder.plot_lr(show_moms=True)\n",
    "Learner.plot = plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.004\n",
    "epochs = 20\n",
    "moms = (0.95,0.95)\n",
    "start_pct = 0.4\n",
    "size=128\n",
    "bs=64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NewResBlock(Module):\n",
    "    def __init__(self, expansion, ni, nh, stride=1, \n",
    "                 conv_layer=ConvLayer, act_fn=act_fn, bn_1st=True,\n",
    "                 pool=nn.AvgPool2d(2, ceil_mode=True), sa=False,sym=False, zero_bn=True):\n",
    "        nf,ni = nh*expansion,ni*expansion\n",
    "        self.reduce = noop if stride==1 else pool\n",
    "        layers  = [(f\"conv_0\", conv_layer(ni, nh, 3, stride=stride, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\", conv_layer(nh, nf, 3, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ] if expansion == 1 else [\n",
    "                   (f\"conv_0\",conv_layer(ni, nh, 1, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\",conv_layer(nh, nh, 3, stride=1, act_fn=act_fn, bn_1st=bn_1st)), #!!!\n",
    "                   (f\"conv_2\",conv_layer(nh, nf, 1, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ]\n",
    "        if sa: layers.append(('sa', SimpleSelfAttention(nf,ks=1,sym=sym)))\n",
    "        self.convs = nn.Sequential(OrderedDict(layers))\n",
    "        self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False)\n",
    "        self.merge =act_fn\n",
    "\n",
    "    def forward(self, x): \n",
    "        o = self.reduce(x)\n",
    "        return self.merge(self.convs(o) + self.idconv(o))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xresnet50(c_out=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.block = NewResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool = MaxBlurPool2d(3, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.pool = pool\n",
    "model.stem_pool = pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mish activation loaded...\n"
     ]
    }
   ],
   "source": [
    "# model.stem_sizes = [3,32,32,64]\n",
    "model.stem_sizes = [3,32,64,64]\n",
    "\n",
    "model.act_fn= Mish()\n",
    "model.sa = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## repr model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  model xresnet50\n",
       "  (stem): Sequential(\n",
       "    (conv_0): ConvLayer(\n",
       "      (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_1): ConvLayer(\n",
       "      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_2): ConvLayer(\n",
       "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (stem_pool): MaxBlurPool2d()\n",
       "  )\n",
       "  (body): Sequential(\n",
       "    (l_0): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "          (sa): SimpleSelfAttention(\n",
       "            (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_1): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_2): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_4): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_5): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_3): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (head): Sequential(\n",
       "    (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "    (flat): Flatten()\n",
       "    (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (conv_0): ConvLayer(\n",
       "    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_1): ConvLayer(\n",
       "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_2): ConvLayer(\n",
       "    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (stem_pool): MaxBlurPool2d()\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (l_0): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (sa): SimpleSelfAttention(\n",
       "          (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_1): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_2): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_4): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_5): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_3): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "  (flat): Flatten()\n",
       "  (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 80"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## start 0.4 moms 0.95-0.85 -- 8761"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_pct = 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "moms = (0.95, 0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixup = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.100685</td>\n",
       "      <td>2.041770</td>\n",
       "      <td>0.313311</td>\n",
       "      <td>0.848053</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.863917</td>\n",
       "      <td>1.587333</td>\n",
       "      <td>0.516671</td>\n",
       "      <td>0.924917</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.736845</td>\n",
       "      <td>1.804782</td>\n",
       "      <td>0.510562</td>\n",
       "      <td>0.872487</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.626959</td>\n",
       "      <td>1.427368</td>\n",
       "      <td>0.604225</td>\n",
       "      <td>0.935353</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.568612</td>\n",
       "      <td>1.709640</td>\n",
       "      <td>0.503436</td>\n",
       "      <td>0.912191</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.493422</td>\n",
       "      <td>1.283897</td>\n",
       "      <td>0.667091</td>\n",
       "      <td>0.952405</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.439678</td>\n",
       "      <td>1.436508</td>\n",
       "      <td>0.621787</td>\n",
       "      <td>0.936116</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.382217</td>\n",
       "      <td>1.119012</td>\n",
       "      <td>0.745228</td>\n",
       "      <td>0.969203</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.356275</td>\n",
       "      <td>1.311639</td>\n",
       "      <td>0.664291</td>\n",
       "      <td>0.944515</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.307383</td>\n",
       "      <td>1.078042</td>\n",
       "      <td>0.763044</td>\n",
       "      <td>0.969712</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.271956</td>\n",
       "      <td>1.263712</td>\n",
       "      <td>0.695597</td>\n",
       "      <td>0.962331</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.247519</td>\n",
       "      <td>1.044061</td>\n",
       "      <td>0.777043</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.220320</td>\n",
       "      <td>1.112821</td>\n",
       "      <td>0.757699</td>\n",
       "      <td>0.965131</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.185006</td>\n",
       "      <td>0.998634</td>\n",
       "      <td>0.800967</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.186747</td>\n",
       "      <td>1.078120</td>\n",
       "      <td>0.760499</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.157057</td>\n",
       "      <td>0.979302</td>\n",
       "      <td>0.795622</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.125715</td>\n",
       "      <td>1.020656</td>\n",
       "      <td>0.780860</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.127583</td>\n",
       "      <td>0.937193</td>\n",
       "      <td>0.821838</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.101889</td>\n",
       "      <td>1.017577</td>\n",
       "      <td>0.786460</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.094799</td>\n",
       "      <td>0.958100</td>\n",
       "      <td>0.813948</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.068879</td>\n",
       "      <td>0.986024</td>\n",
       "      <td>0.805294</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.056258</td>\n",
       "      <td>0.929158</td>\n",
       "      <td>0.830746</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.037072</td>\n",
       "      <td>0.944093</td>\n",
       "      <td>0.823874</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.038256</td>\n",
       "      <td>0.921299</td>\n",
       "      <td>0.834563</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.030879</td>\n",
       "      <td>1.008018</td>\n",
       "      <td>0.794350</td>\n",
       "      <td>0.972512</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.998462</td>\n",
       "      <td>0.901868</td>\n",
       "      <td>0.837109</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.010875</td>\n",
       "      <td>0.994117</td>\n",
       "      <td>0.801731</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.992761</td>\n",
       "      <td>0.898179</td>\n",
       "      <td>0.843726</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.985362</td>\n",
       "      <td>0.949988</td>\n",
       "      <td>0.826164</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.989854</td>\n",
       "      <td>0.936009</td>\n",
       "      <td>0.826164</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.984507</td>\n",
       "      <td>0.943148</td>\n",
       "      <td>0.823365</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.969782</td>\n",
       "      <td>0.919524</td>\n",
       "      <td>0.833291</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.976551</td>\n",
       "      <td>1.148591</td>\n",
       "      <td>0.763044</td>\n",
       "      <td>0.958768</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.980251</td>\n",
       "      <td>0.904546</td>\n",
       "      <td>0.840926</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.981949</td>\n",
       "      <td>1.082943</td>\n",
       "      <td>0.763299</td>\n",
       "      <td>0.964877</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.961302</td>\n",
       "      <td>0.909303</td>\n",
       "      <td>0.834309</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.957279</td>\n",
       "      <td>1.005264</td>\n",
       "      <td>0.808857</td>\n",
       "      <td>0.969712</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.957952</td>\n",
       "      <td>0.899054</td>\n",
       "      <td>0.841945</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.942493</td>\n",
       "      <td>1.023756</td>\n",
       "      <td>0.790786</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.933221</td>\n",
       "      <td>0.905782</td>\n",
       "      <td>0.839399</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.922539</td>\n",
       "      <td>1.014396</td>\n",
       "      <td>0.792314</td>\n",
       "      <td>0.969203</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.926757</td>\n",
       "      <td>0.878320</td>\n",
       "      <td>0.857470</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.921291</td>\n",
       "      <td>0.932555</td>\n",
       "      <td>0.829728</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.917643</td>\n",
       "      <td>0.916889</td>\n",
       "      <td>0.839145</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.895542</td>\n",
       "      <td>0.925107</td>\n",
       "      <td>0.835836</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.912408</td>\n",
       "      <td>0.886191</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.906581</td>\n",
       "      <td>0.940018</td>\n",
       "      <td>0.839654</td>\n",
       "      <td>0.971749</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.896247</td>\n",
       "      <td>0.873844</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.896961</td>\n",
       "      <td>0.934028</td>\n",
       "      <td>0.839908</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.885456</td>\n",
       "      <td>0.875445</td>\n",
       "      <td>0.854416</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.876381</td>\n",
       "      <td>0.881520</td>\n",
       "      <td>0.848817</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.862022</td>\n",
       "      <td>0.886714</td>\n",
       "      <td>0.851871</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.865023</td>\n",
       "      <td>0.872022</td>\n",
       "      <td>0.854161</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.866916</td>\n",
       "      <td>0.875140</td>\n",
       "      <td>0.855688</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.856747</td>\n",
       "      <td>0.871325</td>\n",
       "      <td>0.856707</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.858829</td>\n",
       "      <td>0.844902</td>\n",
       "      <td>0.865615</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.840499</td>\n",
       "      <td>0.884912</td>\n",
       "      <td>0.853398</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.849040</td>\n",
       "      <td>0.852517</td>\n",
       "      <td>0.863833</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.848569</td>\n",
       "      <td>0.872146</td>\n",
       "      <td>0.855688</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.836626</td>\n",
       "      <td>0.868005</td>\n",
       "      <td>0.858488</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.843070</td>\n",
       "      <td>0.870838</td>\n",
       "      <td>0.851616</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.828042</td>\n",
       "      <td>0.858739</td>\n",
       "      <td>0.862560</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.827650</td>\n",
       "      <td>0.842093</td>\n",
       "      <td>0.870196</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.821870</td>\n",
       "      <td>0.850204</td>\n",
       "      <td>0.859761</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.831768</td>\n",
       "      <td>0.834880</td>\n",
       "      <td>0.872996</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.823270</td>\n",
       "      <td>0.832519</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.821401</td>\n",
       "      <td>0.838419</td>\n",
       "      <td>0.873505</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.823241</td>\n",
       "      <td>0.837436</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.823870</td>\n",
       "      <td>0.833174</td>\n",
       "      <td>0.873759</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.819921</td>\n",
       "      <td>0.829036</td>\n",
       "      <td>0.873505</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.806560</td>\n",
       "      <td>0.831436</td>\n",
       "      <td>0.869432</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.808051</td>\n",
       "      <td>0.825075</td>\n",
       "      <td>0.875286</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.815564</td>\n",
       "      <td>0.828413</td>\n",
       "      <td>0.871723</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.814681</td>\n",
       "      <td>0.822981</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.818357</td>\n",
       "      <td>0.824609</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.823064</td>\n",
       "      <td>0.821988</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.804440</td>\n",
       "      <td>0.819962</td>\n",
       "      <td>0.878850</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.821698</td>\n",
       "      <td>0.820633</td>\n",
       "      <td>0.876813</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.813997</td>\n",
       "      <td>0.820368</td>\n",
       "      <td>0.877832</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.811580</td>\n",
       "      <td>0.820870</td>\n",
       "      <td>0.878086</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.096216</td>\n",
       "      <td>1.951555</td>\n",
       "      <td>0.398829</td>\n",
       "      <td>0.842963</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.900454</td>\n",
       "      <td>1.733743</td>\n",
       "      <td>0.455587</td>\n",
       "      <td>0.885976</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.752440</td>\n",
       "      <td>1.729514</td>\n",
       "      <td>0.449987</td>\n",
       "      <td>0.892339</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.624246</td>\n",
       "      <td>1.382082</td>\n",
       "      <td>0.622041</td>\n",
       "      <td>0.942988</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.561089</td>\n",
       "      <td>1.430689</td>\n",
       "      <td>0.599135</td>\n",
       "      <td>0.934334</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.501861</td>\n",
       "      <td>1.238758</td>\n",
       "      <td>0.695597</td>\n",
       "      <td>0.959277</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.430757</td>\n",
       "      <td>1.267634</td>\n",
       "      <td>0.680580</td>\n",
       "      <td>0.952405</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.400266</td>\n",
       "      <td>1.133571</td>\n",
       "      <td>0.739628</td>\n",
       "      <td>0.966913</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.348062</td>\n",
       "      <td>1.212405</td>\n",
       "      <td>0.705778</td>\n",
       "      <td>0.959023</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.308680</td>\n",
       "      <td>1.085571</td>\n",
       "      <td>0.762026</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.270070</td>\n",
       "      <td>1.103612</td>\n",
       "      <td>0.757445</td>\n",
       "      <td>0.968185</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.243296</td>\n",
       "      <td>1.013309</td>\n",
       "      <td>0.798422</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.211820</td>\n",
       "      <td>1.105311</td>\n",
       "      <td>0.744973</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.213865</td>\n",
       "      <td>0.990241</td>\n",
       "      <td>0.803003</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.173291</td>\n",
       "      <td>1.119529</td>\n",
       "      <td>0.739119</td>\n",
       "      <td>0.968440</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.154837</td>\n",
       "      <td>0.969539</td>\n",
       "      <td>0.809875</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.131310</td>\n",
       "      <td>1.074347</td>\n",
       "      <td>0.768898</td>\n",
       "      <td>0.967931</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.112964</td>\n",
       "      <td>0.947948</td>\n",
       "      <td>0.821838</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.102111</td>\n",
       "      <td>0.993536</td>\n",
       "      <td>0.803258</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.098813</td>\n",
       "      <td>0.941137</td>\n",
       "      <td>0.822856</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.075356</td>\n",
       "      <td>1.001838</td>\n",
       "      <td>0.803767</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.039151</td>\n",
       "      <td>0.955313</td>\n",
       "      <td>0.817765</td>\n",
       "      <td>0.972512</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.057633</td>\n",
       "      <td>0.974829</td>\n",
       "      <td>0.809366</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.031107</td>\n",
       "      <td>0.913867</td>\n",
       "      <td>0.838127</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.019567</td>\n",
       "      <td>0.974578</td>\n",
       "      <td>0.808603</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.025044</td>\n",
       "      <td>0.924362</td>\n",
       "      <td>0.834054</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.001503</td>\n",
       "      <td>0.977089</td>\n",
       "      <td>0.806567</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.005383</td>\n",
       "      <td>0.900449</td>\n",
       "      <td>0.847544</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.998329</td>\n",
       "      <td>1.001089</td>\n",
       "      <td>0.799949</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.993583</td>\n",
       "      <td>0.919841</td>\n",
       "      <td>0.831509</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.964532</td>\n",
       "      <td>0.964047</td>\n",
       "      <td>0.814711</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.972545</td>\n",
       "      <td>0.913476</td>\n",
       "      <td>0.835836</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.999240</td>\n",
       "      <td>1.029155</td>\n",
       "      <td>0.791295</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.988154</td>\n",
       "      <td>0.931049</td>\n",
       "      <td>0.826673</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.982109</td>\n",
       "      <td>0.988006</td>\n",
       "      <td>0.801222</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.970155</td>\n",
       "      <td>0.914916</td>\n",
       "      <td>0.836854</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.946699</td>\n",
       "      <td>0.997790</td>\n",
       "      <td>0.816493</td>\n",
       "      <td>0.969203</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.944673</td>\n",
       "      <td>0.896153</td>\n",
       "      <td>0.848307</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.938254</td>\n",
       "      <td>0.973906</td>\n",
       "      <td>0.821583</td>\n",
       "      <td>0.972512</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.929679</td>\n",
       "      <td>0.901306</td>\n",
       "      <td>0.843472</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.934751</td>\n",
       "      <td>0.949460</td>\n",
       "      <td>0.828201</td>\n",
       "      <td>0.972512</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.927147</td>\n",
       "      <td>0.886501</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.909193</td>\n",
       "      <td>0.909705</td>\n",
       "      <td>0.839145</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.914117</td>\n",
       "      <td>0.912419</td>\n",
       "      <td>0.837109</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.916644</td>\n",
       "      <td>0.931066</td>\n",
       "      <td>0.837618</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.908529</td>\n",
       "      <td>0.899076</td>\n",
       "      <td>0.843472</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.888556</td>\n",
       "      <td>0.953588</td>\n",
       "      <td>0.824383</td>\n",
       "      <td>0.967931</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.889632</td>\n",
       "      <td>0.885308</td>\n",
       "      <td>0.849071</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.885090</td>\n",
       "      <td>0.943577</td>\n",
       "      <td>0.832527</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.880726</td>\n",
       "      <td>0.884265</td>\n",
       "      <td>0.852380</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.879022</td>\n",
       "      <td>0.924787</td>\n",
       "      <td>0.839399</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.877179</td>\n",
       "      <td>0.864047</td>\n",
       "      <td>0.857979</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.862976</td>\n",
       "      <td>0.903812</td>\n",
       "      <td>0.851107</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.865236</td>\n",
       "      <td>0.853163</td>\n",
       "      <td>0.866124</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.864460</td>\n",
       "      <td>0.887338</td>\n",
       "      <td>0.848562</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.851695</td>\n",
       "      <td>0.863603</td>\n",
       "      <td>0.856452</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.855430</td>\n",
       "      <td>0.893368</td>\n",
       "      <td>0.848053</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.847590</td>\n",
       "      <td>0.870458</td>\n",
       "      <td>0.854670</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.850009</td>\n",
       "      <td>0.884021</td>\n",
       "      <td>0.850089</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.847950</td>\n",
       "      <td>0.860497</td>\n",
       "      <td>0.859761</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.834978</td>\n",
       "      <td>0.853336</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.834315</td>\n",
       "      <td>0.847236</td>\n",
       "      <td>0.865615</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.834026</td>\n",
       "      <td>0.852513</td>\n",
       "      <td>0.864851</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.833202</td>\n",
       "      <td>0.849251</td>\n",
       "      <td>0.862306</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.826143</td>\n",
       "      <td>0.847098</td>\n",
       "      <td>0.869178</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.830506</td>\n",
       "      <td>0.843125</td>\n",
       "      <td>0.865869</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.830192</td>\n",
       "      <td>0.826031</td>\n",
       "      <td>0.868923</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.816467</td>\n",
       "      <td>0.828102</td>\n",
       "      <td>0.871469</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.817020</td>\n",
       "      <td>0.826775</td>\n",
       "      <td>0.875286</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.826446</td>\n",
       "      <td>0.828892</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.815533</td>\n",
       "      <td>0.827007</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.811099</td>\n",
       "      <td>0.822595</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.813184</td>\n",
       "      <td>0.821586</td>\n",
       "      <td>0.872741</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.820345</td>\n",
       "      <td>0.821044</td>\n",
       "      <td>0.875286</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.817671</td>\n",
       "      <td>0.822321</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.805268</td>\n",
       "      <td>0.821003</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.806063</td>\n",
       "      <td>0.820699</td>\n",
       "      <td>0.877322</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.805521</td>\n",
       "      <td>0.821092</td>\n",
       "      <td>0.876304</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.815352</td>\n",
       "      <td>0.821133</td>\n",
       "      <td>0.875286</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.821339</td>\n",
       "      <td>0.821060</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.107181</td>\n",
       "      <td>2.056730</td>\n",
       "      <td>0.310003</td>\n",
       "      <td>0.812930</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.912955</td>\n",
       "      <td>1.633159</td>\n",
       "      <td>0.490710</td>\n",
       "      <td>0.905574</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.746326</td>\n",
       "      <td>1.754409</td>\n",
       "      <td>0.471367</td>\n",
       "      <td>0.913719</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.635498</td>\n",
       "      <td>1.344010</td>\n",
       "      <td>0.649020</td>\n",
       "      <td>0.945024</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.554744</td>\n",
       "      <td>1.465911</td>\n",
       "      <td>0.591499</td>\n",
       "      <td>0.922881</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.486863</td>\n",
       "      <td>1.200724</td>\n",
       "      <td>0.709341</td>\n",
       "      <td>0.960550</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.417915</td>\n",
       "      <td>1.231970</td>\n",
       "      <td>0.705269</td>\n",
       "      <td>0.958005</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.384941</td>\n",
       "      <td>1.119324</td>\n",
       "      <td>0.747009</td>\n",
       "      <td>0.968949</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.328289</td>\n",
       "      <td>1.129901</td>\n",
       "      <td>0.741919</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.296810</td>\n",
       "      <td>1.017716</td>\n",
       "      <td>0.793332</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.275647</td>\n",
       "      <td>1.115632</td>\n",
       "      <td>0.751845</td>\n",
       "      <td>0.968185</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.237317</td>\n",
       "      <td>1.013105</td>\n",
       "      <td>0.791805</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.191108</td>\n",
       "      <td>1.056382</td>\n",
       "      <td>0.780860</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.185230</td>\n",
       "      <td>0.974034</td>\n",
       "      <td>0.812930</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.163430</td>\n",
       "      <td>1.029521</td>\n",
       "      <td>0.783915</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.133454</td>\n",
       "      <td>0.970086</td>\n",
       "      <td>0.808094</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.138091</td>\n",
       "      <td>0.986969</td>\n",
       "      <td>0.805039</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.116126</td>\n",
       "      <td>0.943159</td>\n",
       "      <td>0.818529</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.092012</td>\n",
       "      <td>0.973945</td>\n",
       "      <td>0.811911</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.077129</td>\n",
       "      <td>0.925662</td>\n",
       "      <td>0.829982</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.064059</td>\n",
       "      <td>1.002814</td>\n",
       "      <td>0.800204</td>\n",
       "      <td>0.969967</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.055525</td>\n",
       "      <td>0.928527</td>\n",
       "      <td>0.829219</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.039017</td>\n",
       "      <td>0.932492</td>\n",
       "      <td>0.834054</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.031054</td>\n",
       "      <td>0.920268</td>\n",
       "      <td>0.826673</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.012525</td>\n",
       "      <td>0.952257</td>\n",
       "      <td>0.818529</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.001081</td>\n",
       "      <td>0.914812</td>\n",
       "      <td>0.832018</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.985127</td>\n",
       "      <td>0.924372</td>\n",
       "      <td>0.838636</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.996303</td>\n",
       "      <td>0.914376</td>\n",
       "      <td>0.839908</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.994142</td>\n",
       "      <td>1.019517</td>\n",
       "      <td>0.798422</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.993709</td>\n",
       "      <td>0.918503</td>\n",
       "      <td>0.837872</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.973421</td>\n",
       "      <td>0.946195</td>\n",
       "      <td>0.815475</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.955243</td>\n",
       "      <td>0.893323</td>\n",
       "      <td>0.845508</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.995807</td>\n",
       "      <td>1.011399</td>\n",
       "      <td>0.799440</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.972744</td>\n",
       "      <td>0.892504</td>\n",
       "      <td>0.848307</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.969950</td>\n",
       "      <td>1.035074</td>\n",
       "      <td>0.798422</td>\n",
       "      <td>0.966658</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.972218</td>\n",
       "      <td>0.924043</td>\n",
       "      <td>0.835073</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.946748</td>\n",
       "      <td>0.997349</td>\n",
       "      <td>0.812166</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.950621</td>\n",
       "      <td>0.902788</td>\n",
       "      <td>0.844490</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.937280</td>\n",
       "      <td>1.046609</td>\n",
       "      <td>0.778570</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.945642</td>\n",
       "      <td>0.897411</td>\n",
       "      <td>0.848562</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.930296</td>\n",
       "      <td>0.985163</td>\n",
       "      <td>0.813693</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.919786</td>\n",
       "      <td>0.887474</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.913729</td>\n",
       "      <td>0.991329</td>\n",
       "      <td>0.802749</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.899599</td>\n",
       "      <td>0.888903</td>\n",
       "      <td>0.844999</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.910890</td>\n",
       "      <td>0.934823</td>\n",
       "      <td>0.826419</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.892584</td>\n",
       "      <td>0.879937</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.903192</td>\n",
       "      <td>0.945883</td>\n",
       "      <td>0.833036</td>\n",
       "      <td>0.970221</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.901632</td>\n",
       "      <td>0.889845</td>\n",
       "      <td>0.848817</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.881509</td>\n",
       "      <td>0.911410</td>\n",
       "      <td>0.842708</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.893361</td>\n",
       "      <td>0.897925</td>\n",
       "      <td>0.841181</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.869182</td>\n",
       "      <td>0.890868</td>\n",
       "      <td>0.849071</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.868360</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.874492</td>\n",
       "      <td>0.877632</td>\n",
       "      <td>0.853398</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.868640</td>\n",
       "      <td>0.868584</td>\n",
       "      <td>0.860779</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.861833</td>\n",
       "      <td>0.874457</td>\n",
       "      <td>0.854925</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.853476</td>\n",
       "      <td>0.858884</td>\n",
       "      <td>0.860270</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.854677</td>\n",
       "      <td>0.884181</td>\n",
       "      <td>0.854416</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.848244</td>\n",
       "      <td>0.846458</td>\n",
       "      <td>0.862306</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.841106</td>\n",
       "      <td>0.852796</td>\n",
       "      <td>0.866378</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.840952</td>\n",
       "      <td>0.838176</td>\n",
       "      <td>0.869941</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.839821</td>\n",
       "      <td>0.840230</td>\n",
       "      <td>0.867905</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.834158</td>\n",
       "      <td>0.841258</td>\n",
       "      <td>0.868669</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.837560</td>\n",
       "      <td>0.835797</td>\n",
       "      <td>0.868160</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.829959</td>\n",
       "      <td>0.823947</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.837716</td>\n",
       "      <td>0.851633</td>\n",
       "      <td>0.861033</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.820361</td>\n",
       "      <td>0.832388</td>\n",
       "      <td>0.864342</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.825806</td>\n",
       "      <td>0.830367</td>\n",
       "      <td>0.869432</td>\n",
       "      <td>0.982693</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.832853</td>\n",
       "      <td>0.822590</td>\n",
       "      <td>0.868669</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.815315</td>\n",
       "      <td>0.816967</td>\n",
       "      <td>0.871469</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.813315</td>\n",
       "      <td>0.817020</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.816130</td>\n",
       "      <td>0.821125</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.815792</td>\n",
       "      <td>0.816122</td>\n",
       "      <td>0.876813</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.816300</td>\n",
       "      <td>0.817560</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.815448</td>\n",
       "      <td>0.815325</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.811181</td>\n",
       "      <td>0.815362</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.814111</td>\n",
       "      <td>0.812697</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.809025</td>\n",
       "      <td>0.812061</td>\n",
       "      <td>0.875795</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.806608</td>\n",
       "      <td>0.812900</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.809826</td>\n",
       "      <td>0.811715</td>\n",
       "      <td>0.875795</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.813589</td>\n",
       "      <td>0.812851</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.068183</td>\n",
       "      <td>1.880453</td>\n",
       "      <td>0.358361</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.888131</td>\n",
       "      <td>1.672836</td>\n",
       "      <td>0.490201</td>\n",
       "      <td>0.906592</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.750591</td>\n",
       "      <td>1.758852</td>\n",
       "      <td>0.457623</td>\n",
       "      <td>0.880122</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.651563</td>\n",
       "      <td>1.367432</td>\n",
       "      <td>0.628913</td>\n",
       "      <td>0.945533</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.547584</td>\n",
       "      <td>1.469544</td>\n",
       "      <td>0.575974</td>\n",
       "      <td>0.939170</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.489463</td>\n",
       "      <td>1.262687</td>\n",
       "      <td>0.688979</td>\n",
       "      <td>0.959023</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.439909</td>\n",
       "      <td>1.379263</td>\n",
       "      <td>0.620260</td>\n",
       "      <td>0.949860</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.384773</td>\n",
       "      <td>1.130723</td>\n",
       "      <td>0.751591</td>\n",
       "      <td>0.971240</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.347175</td>\n",
       "      <td>1.216598</td>\n",
       "      <td>0.707559</td>\n",
       "      <td>0.957241</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>1.070212</td>\n",
       "      <td>0.764317</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.289364</td>\n",
       "      <td>1.169240</td>\n",
       "      <td>0.729957</td>\n",
       "      <td>0.959786</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.251327</td>\n",
       "      <td>1.013050</td>\n",
       "      <td>0.794604</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.225354</td>\n",
       "      <td>1.136886</td>\n",
       "      <td>0.735811</td>\n",
       "      <td>0.962840</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.204486</td>\n",
       "      <td>0.983395</td>\n",
       "      <td>0.809621</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.179990</td>\n",
       "      <td>1.022394</td>\n",
       "      <td>0.790023</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.148633</td>\n",
       "      <td>0.962447</td>\n",
       "      <td>0.814202</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.123648</td>\n",
       "      <td>1.034357</td>\n",
       "      <td>0.782387</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.123393</td>\n",
       "      <td>0.966174</td>\n",
       "      <td>0.809366</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.099392</td>\n",
       "      <td>0.995540</td>\n",
       "      <td>0.795368</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.076071</td>\n",
       "      <td>0.941947</td>\n",
       "      <td>0.823619</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.076946</td>\n",
       "      <td>0.941194</td>\n",
       "      <td>0.819038</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.049303</td>\n",
       "      <td>0.925287</td>\n",
       "      <td>0.830237</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.041799</td>\n",
       "      <td>0.956856</td>\n",
       "      <td>0.811911</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.033268</td>\n",
       "      <td>0.925145</td>\n",
       "      <td>0.823619</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.020908</td>\n",
       "      <td>0.926708</td>\n",
       "      <td>0.828201</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.003030</td>\n",
       "      <td>0.922082</td>\n",
       "      <td>0.832273</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.010230</td>\n",
       "      <td>0.938686</td>\n",
       "      <td>0.823874</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.992873</td>\n",
       "      <td>0.941184</td>\n",
       "      <td>0.826673</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.970862</td>\n",
       "      <td>0.984329</td>\n",
       "      <td>0.809366</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.991331</td>\n",
       "      <td>0.921668</td>\n",
       "      <td>0.834563</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.971305</td>\n",
       "      <td>0.925727</td>\n",
       "      <td>0.831509</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.975372</td>\n",
       "      <td>0.922859</td>\n",
       "      <td>0.834563</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.983605</td>\n",
       "      <td>0.967404</td>\n",
       "      <td>0.820820</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.983538</td>\n",
       "      <td>0.903571</td>\n",
       "      <td>0.839145</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.960231</td>\n",
       "      <td>1.090354</td>\n",
       "      <td>0.769407</td>\n",
       "      <td>0.967676</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.951129</td>\n",
       "      <td>0.911289</td>\n",
       "      <td>0.840672</td>\n",
       "      <td>0.971749</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.957658</td>\n",
       "      <td>0.984755</td>\n",
       "      <td>0.812166</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.966560</td>\n",
       "      <td>0.899153</td>\n",
       "      <td>0.843981</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.941148</td>\n",
       "      <td>1.030457</td>\n",
       "      <td>0.786714</td>\n",
       "      <td>0.967931</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.930338</td>\n",
       "      <td>0.901534</td>\n",
       "      <td>0.843726</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.933912</td>\n",
       "      <td>1.021502</td>\n",
       "      <td>0.799695</td>\n",
       "      <td>0.966149</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.922362</td>\n",
       "      <td>0.891017</td>\n",
       "      <td>0.844490</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.918222</td>\n",
       "      <td>1.032061</td>\n",
       "      <td>0.798422</td>\n",
       "      <td>0.970221</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.907075</td>\n",
       "      <td>0.897152</td>\n",
       "      <td>0.848307</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.896680</td>\n",
       "      <td>0.926693</td>\n",
       "      <td>0.838381</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.902960</td>\n",
       "      <td>0.894019</td>\n",
       "      <td>0.845762</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.901464</td>\n",
       "      <td>0.934583</td>\n",
       "      <td>0.829728</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.879929</td>\n",
       "      <td>0.907484</td>\n",
       "      <td>0.840672</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.888190</td>\n",
       "      <td>0.910179</td>\n",
       "      <td>0.843981</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.876978</td>\n",
       "      <td>0.900995</td>\n",
       "      <td>0.847289</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.868512</td>\n",
       "      <td>0.919113</td>\n",
       "      <td>0.841435</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.882227</td>\n",
       "      <td>0.864484</td>\n",
       "      <td>0.857216</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.864063</td>\n",
       "      <td>0.871948</td>\n",
       "      <td>0.852634</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.854478</td>\n",
       "      <td>0.868619</td>\n",
       "      <td>0.857725</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.865158</td>\n",
       "      <td>0.867358</td>\n",
       "      <td>0.859252</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.857063</td>\n",
       "      <td>0.867701</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.851268</td>\n",
       "      <td>0.873050</td>\n",
       "      <td>0.856707</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.854455</td>\n",
       "      <td>0.877437</td>\n",
       "      <td>0.854925</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.837240</td>\n",
       "      <td>0.854346</td>\n",
       "      <td>0.864088</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.835599</td>\n",
       "      <td>0.853103</td>\n",
       "      <td>0.863324</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.833761</td>\n",
       "      <td>0.855653</td>\n",
       "      <td>0.864088</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.838089</td>\n",
       "      <td>0.846466</td>\n",
       "      <td>0.866887</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.838003</td>\n",
       "      <td>0.862723</td>\n",
       "      <td>0.862306</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.822882</td>\n",
       "      <td>0.839574</td>\n",
       "      <td>0.868669</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.827476</td>\n",
       "      <td>0.850664</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.826018</td>\n",
       "      <td>0.840400</td>\n",
       "      <td>0.868923</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.837067</td>\n",
       "      <td>0.835172</td>\n",
       "      <td>0.869432</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.824646</td>\n",
       "      <td>0.831379</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.825467</td>\n",
       "      <td>0.830661</td>\n",
       "      <td>0.874268</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.812866</td>\n",
       "      <td>0.830566</td>\n",
       "      <td>0.869432</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.816366</td>\n",
       "      <td>0.828370</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.822490</td>\n",
       "      <td>0.829379</td>\n",
       "      <td>0.871723</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.814842</td>\n",
       "      <td>0.824666</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.812675</td>\n",
       "      <td>0.824233</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.819189</td>\n",
       "      <td>0.825825</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.807086</td>\n",
       "      <td>0.827436</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.823635</td>\n",
       "      <td>0.827095</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.809053</td>\n",
       "      <td>0.824605</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.816359</td>\n",
       "      <td>0.823575</td>\n",
       "      <td>0.877832</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.809653</td>\n",
       "      <td>0.824701</td>\n",
       "      <td>0.874777</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vHYTmGyR8u6J2NlqPTYshOymauXnJvLhqH+GhDjwGLj4hE4ApWfEAbC5rYPboZPu11UYxJTOe2MhQosNDWF5czQXTMlhfYrVHzB9jBYl5eck8+nER60pqOXFsKmv31pESE86oo2ibUWqgaElC9cuLq0rIS43hnW+dwjvfOoUrZ2dT3tDKj7+Yzw0n5XnTfeP0sYxOieZHb2ymtcPND17bxBvr9/O98ybyjdPHHjZA+JqSmcDYtFhv1VZOcjT3nDuRj3ZU8sb6/TS0dvDy6hIump7Zo2rrcObmJfPGXSfxj5vmeucNio8MIz0uomdJoqKJyDAHmXa30avm5LCnupk/f7CL/Ix4xqVb3UVHxkeSEhPepfF68/4GshKjSIoJJyzEQUFuMsvsksSK4hpEYE6eFVBm5yYhcqgr7Lp9tcwclfS51lRWyt80SAwj60vquP7JlTjbel+1qy+FFU2s3FPDV+bkICJMzojnZ5dNY93953KjT4AAq1rngYunUFzp5NKHP+OFVSXcecbYXgeTHa3rT8zlhJxEfvLmFh75qAhnu5ubT8478oHdTMlMILXbwLlx6bEUVnavbmpiTGqst7fWBVMziIsMpba5g0vsUgRY01ZMyUrwVjEBbCmzptvoNH9MMjvLm6hqsno65WfEkxBldXmNjwwjPyOelbtrqHG2U1zl7HejtVIDTYPEELOnyonH0/ukjb9ZtIOPd1ayqFvvoU7tLg9tLneP7S+u2keoQ7hiVna/8nDGxHTOnzKS7QcbueHEXO45d2L/L+AwQhzCr66cTlObi0c+KmJuXjJTsxKOfGA/jE2LpaiiCd8JL4sqnYxNPzQmISo8hMtmZiECF83I7HL81Mx4dpY30uZy09jaQXGVk6k+6zAssKuWluyqZO2+Wm9VU6e5ecms3VfrLU0cTaO1UgNJg8QQUtXUxtm/+5jfLd7ZY9/msno+LbSmgXhj/f5ej//Oi+s5+3cfU910qLtpm8vNv9aWcU7+iF776Pfll1dM5y/XzOL+i/L9Wm0yYUQc3zjdKpXc8jlKEX0Zlx5LU5uL8gbr2ls73JTUNjM2retiON87byKv3H4iWYldRy5PzUrA5THsPNjEtgON3m2dpmUlEBMewl8/LqbN5WFeXtcJ8+blJdPa4eEfS/cQ4hCmZ/sn+Cl1rAIWJETkfBHZISKFInJvL/tHiciHIrJORDaKyBcCkc9gsrO8EZfH8NiSYkpqmrvs++snxcRGhHLt/FF8WljVJRAAlNQ08/bmA5TUtPDN59d5xwws3lpOjbOdr8zJOaq8JESH8YVpGZ97FPXhfPus8fz77pM512dw2rHqbLzubKzeU+3EGKuE4SsuMqzXXkdTMu3G6/31hxqtfaqbQkMczMlLZvvBRkSskoOvAnuW1WXF1UzOiPN2pVUq0AISJEQkBHgYuADIB64WkfxuyX4IvGSMmQlcBfzl+OYy+Oy2F4bxeAy/fHe7d3tJTTP/3rifa+aN4tr5o3F7DG9vOtDl2GdX7MMhwvfOm8jSomrv8S+sLCErMYpTxqcdvws5AodDuiyp6Q/du8EWVdjdX7sFib6MSo4mLjKUzWX1bN5fT3pcBOlxXRvUO6ucJo2MJzE6vMu+1NgIb6lltlY1qUEkUCWJuUChMabYGNMOvABc0i2NATq/iiUAvdeRKK9iewqJO04fy1sbD7Bmr1W//fiSYkIcwo0n5TFpZDwTRsSycMOhX2drh5uXVpdw9uR07jxjHNcvGM3fluzm4Q8L+bSwii8X5Az5FcTS4yKIiwg9FCQqm7xTevSHiDAlM57N+xvYUtbQa1tJ53iJ+WN6X5thrj1uYtZRjo9QaiAFKkhkAb7Tapba23w9AFwrIqXA28A3ezuRiNwmIqtFZHVlZeVA5DVoFFc2kZcay+2nj2VEfAQPvrmV6qY2XlxdwqUnZHkne7vkhCxW7an1Dkx7Z/MBapztfG1+LgA/vCifOblJ/HrRDhwCX57TvwbrYCYijE2P7RIkshKjjjhIz9fUzAS2HWigsLKJqZnxPfZPyUzg66eN4Zp5vS8SdPbkdCJCHT2qopQKpEAFid6+lnbvknM18HdjTDbwBeAZEemRX2PMY8aYAmNMQVra4KkSGSi3P7OGH76+qdd9u6ucjEmNITo8lO+fN4kNpfXc9PdVtHZ4uO3UMd50X5xu9cx50y5N/HP5PsakxnCi/U03LMTBw9fMIjMhkvOnjuwxvfRQ5dsNtqiyqd9VTZ2mZiXQ7vLg9him9FKSCHEI910w2Vu11d1Zk0ew/v5zh83vWwWHQAWJUsC3JTSbntVJNwMvARhjlgGRQCrDmDGGTwur+HhnzxJTu8uae2iMXa992cwspmcnsKG0nrMnp3dZJ2BUSjQzRyXyxvr9bNlfz5q9tXx13qgujczpcZF8cM/pPPSVEwb+wgaJcemxVDa2Ud/cQVGF0/u77K8pPqWHz9s192hKLkodD4EKEquA8SKSJyLhWA3TC7ul2QecBSAik7GCxLCuTyqra6GpzUVJTQv1LR1d9u2racbtMd4Hm8Mh/PiLU4iPDO11INslMzLZdqCBn761lcgwB1+a3bP3UmRYyLBapWycXXJYUlhJS4f7qEsSY9JiiQxzkBQdRmY/13FQarALSJAwxriAu4BFwDasXkxbRORBEbnYTvZfwK0isgF4HrjB+I50GoZ2ljd6X/tOJgdWewRAXuqhB9vs0UlsfOA8ZvbSW+bC6Zk4BJYX13DxjMweS28OR50D5xZtsaYTP9ogEeIQ5uQmMy8vRafUUENGwDpjG2PexmqQ9t12v8/rrcBJxztfg9mOg4emjdiyv6HLqN1iu/trf3vjpMVFcNK4VJbsqvI2WA93OUlRhIc4+NCeynxs+tFVNwE89rUCND6ooURH7ASRneWNZCRE4vIYtnZb5GZ3pZPU2HDvfED98Z1zJjA3N5lpOroXsAa85aXGsKO8kbjIUNJi+z/CvJO2KaihRoNEENlxsJGJI+MwBrbs77pcZnGVNRnd0Zg1KknnCOpmXHosO8obu8xAq9RwpnM3BQmX20NhZRMTR8QxJTOewoqmLpPxFVcefW8c1VNnu8TRtkcoNVRpkAgSe2uaaXd5mDAijvzMeFwew65yq42ivrmDamd7v9sjVN86xzB8nvYIpYYiDRJBYudBq2fTxJFx3nmLOtsliqusYDFGv/0es+lZCYQ4hJk5Wg2nFGiQGHT2VDl5fuW+Htt3lFuzh45Lj2V0cjQx4SHedoliey1mrW46drmpMaz90TneeZaUGu40SAwyT3y6m/te3cQeu0trp53ljeSmxBAZFoLDYa0Ot9UeK7G7ykmIQ8hJ0jWR/eFoeogpNdRpkBhkNtulg3c2d109bvvBRiaMOFSdlJ8Zz9b9DXg8huKqJkYlR/d7/WillOovfaoMIi63xzuS+t3Nh9Z7aO1ws6fKyUSf+ZemZMbjbHezr6bZ6tmkjdZKqQGgQSIA6prbe91eVOmktcPDpJFxbCitp7S22d7ehMfAhJGHgkR+htV4vamsnj3VTu3ZpJQaEBokjrPdVU5m/XQxS4uqeuzrXPbyv86dCMC7dpVT55xNviWJ8SNiCXUI728rp7XDoz2blFIDQoPEcbanyonHwAfbKnrs27y/nqiwEM6clM6kkXHeILHjYBPhIQ5yfUoLkWEhjEuPZfFWazI67dmklBoIGiSOs6qmNsBa8L67LWUN5GfGE+IQzp86kjX7aqloaGVneSNj0mIIC+l6u/LtdglA2ySUUgNCg8RxVu202iO2HmigvvnQmhAej2HL/nrvspcXTM3AGFi05aB3zqbu8jOstLERoaTFHf1kdEopdSQaJI6zGjtIGAMrdh8qTeyuduJsd3tXNJswIpYxqTG8sqaUsroWJozoGSQ6R17npcboZHRKqQGhQeI4q2pqIy0ugsgwR5cqp85G684gIWJVOW0otbZP7CVIdJYktD1CKTVQNEgcZ9VN7WQkRDJ7dBLLig4FiS37GwgPdXgnmAOryqlTb9VNCdFh3HxyHpfPyh7YTCulhi0NEsdZjbOdlJhwFoxJYfvBRm/10+ayeiaPjOvSOD01K57spCiiw0PISozq9Xw/uiif0yakHZe8K6WGHw0Sx1l1UxvJMRHeCeRW7q7GGMPmsnqmZHVdIU5EuPus8dx0Uh4Oh7Y5KKWOP12Z7jgyxlDlbCc1NpxpWYlEhYWwrKia/IwEGlpdTMvquYzolwtyApBTpZSyaEniOGpqc9Hu8pASG054qIOC3CSWFVd7J/WbmqlrTSulBhcNEsdRZ/tDSow1pmHB2BR2ljfx8Y5KQh3ChJE6tYZSanDRIHEcVTVZQSI5NhyA+WOsdonX1pcxYUQcEaEhAcubUkr1RoPEcVRtT8mRapckpmUlEBMeQrvLw9Ss+EBmTSmleqVB4jjyVjfZJYmwEAdz8pKBQ4PolFJqMAlYkBCR80Vkh4gUisi9vex/SETW2z87RaQuEPn0p855m5Jjwr3bOqucpmijtVJqEApIF1gRCQEeBs4BSoFVIrLQGLO1M40x5js+6b8JzDzuGfWzqqY2YiNCiQw71PZw9ZxRhIc4mJmTGMCcKaVU7wJVkpgLFBpjio0x7cALwCWHSX818PxxydkAqm5q91Y1dUqIDuOmk3WwnFJqcApUkMgCSnzel9rbehCR0UAe8EEf+28TkdUisrqystLvGfWnzik5lFIqWAQqSPT2tdn0kfYq4BVjjLu3ncaYx4wxBcaYgrS0wT2HUZU9JYdSSgWLQAWJUsB3volsYH8faa9iCFQ1gdVwnRqrJQmlVPAIVJBYBYwXkTwRCccKBAu7JxKRiUASsOw45++YfLijgt1Vzi7bPB5DrbNnm4RSSg1mAQkSxhgXcBewCNgGvGSM2SIiD4rIxT5JrwZeMMb0VRU1KN393Dr++P6uLtsaWjtweYxWNymlgkrAZoE1xrwNvN1t2/3d3j9wPPPkD842F41tLnZVNHbZ3jklh1Y3KaWCiY649rOKRmvqjcKKJjyeQwWgzik5UrQkoZQKIhok/KyioRWA1g4PZXUt3u3dp+RQSqlgoEHCzzpLEmCVJjpVeacJ1yChlAoexxwkRORfInKhiGjAAcrtkgR0DRKd1U1JGiSUUkHEHw/2R4CvArtE5BciMskP5wxalY1thIc4SIkJ79J4XeNsJzE6jLAQjaVKqeBxzE8sY8x/jDHXALOAPcBiEVkqIjeKSNixnj/YVDS2kRYXwfgRsd1KEu1dZn9VSqlg4JevtSKSAtwA3AKsA/6AFTQW++P8waSisZX0+AjGpceyq6KJziEeVU1t3sWGlFIqWPijTeJVYAkQDXzRGHOxMeZFY8w3gWG3aHN5QxvpcRGMT4+jsdVFpd2QXa2jrZVSQcgfg+n+bIzpdYZWY0yBH84fVCoaWjlxbArj0q34WFjRRHp8pDUDrAYJpVSQ8Ud102QR8a6YIyJJInKHH84bdFo73DS0uuyShBUkdlU04XJ7qG1u1yk5lFJBxx9B4lZjjHdpUWNMLXCrH84bdDqrltLjIkmLiyAuMpTCiiZqmzswRqfkUEoFH38ECYeIeNeHsJcmHZZPw4pGa4xEWnwEImI3XjceGm2tJQmlVJDxR5BYBLwkImeJyJlYaz+864fzBp3yBqskMSIuEoDx6bEUVji9A+m0C6xSKtj4I0j8N9bSot8A7gTeB77vh/MGnc55m9LjrRLDuPRYqpraKKq0xktodZNSKtgcc+8mY4wHa9T1I8eeneBW0dhGqENIjraCwfj0OACW764BICVWq5uUUsHlmIOEiIwHfg7kA5Gd240xY4713MGmorGN1NgIHA6riaazG+yK4hocAolRw24AulIqyPmjuukprFKECzgDeBp4xg/nDTrlDa2MiD9UWshKjCIyzEFVUxvJMeHe4KGUUsHCH0EiyhjzPiDGmL32anJn+uG8QaeysY20OG9hCodDGJtmlSa0Z5NSKhj5I0i02tOE7xKRu0TkMiDdD+cdtD7cUcEjHxX12F7R2OZttO7UOahOR1srpYKRP4LEt7HmbbobmA1cC1zvh/MOWv9aU8rvFu+gzeX2bmt3eahxtpMe1zVIjPMGCS1JKKWCzzEFCXvg3JeNMfhd0qkAACAASURBVE3GmFJjzI3GmCuMMcv9lL9BqaHVRYfbsO3AofUiKu2xECPiI7ukHWf3cNIV6ZRSweiYgoQxxg3M9h1xPRzUt3QAsKHEOxvJoTESfZUkNEgopYKQP2aBXQe8ISIvA87OjcaYV/1w7kGpobcg4TNvk6+81BiumTeKs/NHHL8MKqWUn/gjSCQD1XTt0WSAoR8kSnsJEt0arkMcws8um3b8MqeUUn7kjxHXN/ojI8HCGEN9SwfhIQ6KKp00tHYQHxlGZUMrIlqtpJQaWvwx4voprJJDF8aYm4713INRc7sbl8dw0rhkPiusZlNpPSeNS6W8wRptHRrilxVhlVJqUPDHE+0t4N/2z/tAPNB0pINE5HwR2SEihSJybx9pviwiW0Vki4g854e8HrOGVquq6ZTxaQCst9slKhpbezRaK6VUsPNHddO/fN+LyPPAfw53jN119mHgHKAUWCUiC40xW33SjAfuA04yxtSKyKAYoNfZsyknKZq81Bhv43VFY5sGCaXUkDMQdSPjgVFHSDMXKDTGFBtj2oEXgEu6pbkVeNhe6Q5jTIXfc/o51DdbQSI+KpQZ2QlsLK0HOoNE5OEOVUqpoHPMQUJEGkWkofMHeBNrjYnDyQJKfN6X2tt8TQAmiMhnIrJcRM7v4/NvE5HVIrK6srLy815GvzW0ugBIiApjRk4iBxtaKatroaqprcvkfkopNRT4o7op7nMc1tvgu+6N36FYpZLTgWxgiYhM9V1P2/78x4DHAAoKCno0oPtbZ3VTZ5AA+GBbOcZAWryWJJRSQ4s/ShKXiUiCz/tEEbn0CIeVAjk+77OB/b2kecMY02GM2Q3swAoaAdU5RiI+Moz8jHhCHcJ7W8uBnqOtlVIq2PmjTeLHxpj6zjf2N/0fH+GYVcB4EckTkXDgKmBhtzSvY61PgYikYlU/Ffshv8eksyQRHxVGZFgIkzLiWF5cDWiQUEoNPf4IEr2d47DVWMYYF3AXsAjYBrxkjNkiIg+KyMV2skVAtYhsBT4EvmeMqfZDfo9JfUsHcRGhhNgLCM3ITqTDbdVydZ/cTymlgp0/puVYLSK/w+rSaoBvAmuOdJAx5m3g7W7b7vd5bYDv2j+DRkNrB/E+y5DOyEnk2RX7AEjV6cCVUkOMP0oS3wTagReBl4AW4E4/nHdQamjpGiROsBuvk2PCCQ/V0dZKqaHFH72bnECvI6aHovqWDhKiDv3axqbFEh0eou0RSqkhyR+9mxaLSKLP+yQRWXSs5x2sGlpcxEceKkmEOITTJ6YxNSvhMEcppVRw8kebRKrv2IXBNIXGQLBKEmFdtj381VkMs3WXlFLDhD8q0T0i4p2GQ0Ry6WVW2KGityChAUIpNVT5oyTxA+BTEfnYfn8qcJsfzjvotLs8tHS4uzRcK6XUUOaPhut3RaQAKzCsB97A6uE05HROE969JKGUUkOVPxYdugX4FtbUGuuB+cAyui5nOiT4ztuklFLDgT/aJL4FzAH2GmPOAGYCAz8dawB4522K8kctnVJKDX7+CBKtxphWABGJMMZsByb64byDjpYklFLDjT++Epfa4yReBxaLSC09Z3QdEjRIKKWGG380XF9mv3xARD4EEoB3j/W8g1HngkO+g+mUUmoo82vlujHm4yOnCl4NPtOEK6XUcKAz0h2FhpYOwkMdRIaFBDorSil1XGiQOAq9jbZWSqmhTIPEUdAgoZQabjRIHIWG1g7iI3WMhFJq+NAgcRS0JKGUGm40SBwFDRJKqeFGg8RRaGhxafdXpdSwokGinzweQ0OrliSUUsOLBol+amxzYYxOyaGUGl40SPSTd7S1TsmhlBpGNEj0U71OyaGUGoY0SPRTg84Aq5QahjRI9FPn0qW64JBSajgJWJAQkfNFZIeIFIrIvb3sv0FEKkVkvf1zSyDy2UnXklBKDUcB+VosIiHAw8A5QCmwSkQWGmO2dkv6ojHmruOewV5okFBKDUeBKknMBQqNMcXGmHbgBeCSAOWlXxpaXDgEYsK1ukkpNXwEKkhkASU+70vtbd1dISIbReQVEcnp7UQicpuIrBaR1ZWVlQORV8AqScRHheFwyIB9hlJKDTaBChK9PWlNt/dvArnGmOnAf4B/9HYiY8xjxpgCY0xBWlqan7N5iI62VkoNR4EKEqWAb8kgG9jvm8AYU22MabPf/g2YfZzyxoH6Fr770noa7R5NYJckdCCdUmqYCVSQWAWMF5E8EQkHrgIW+iYQkQyftxcD245X5j4rrObVtWX8e+MB7zadAVYpNRwFJEgYY1zAXcAirIf/S8aYLSLyoIhcbCe7W0S2iMgG4G7ghuOVvxqnVYB5yydINGiQUEoNQwHrqmOMeRt4u9u2+31e3wfcd7zzBVDd1A7A0qIqKhvbSIuLoL7FpQPplFLDjo647kVVUzvhIQ48Bt7dfABjDA127yallBpONEj0osbZxoSRsUwYEcubGw7Q5vLQ7vZodZNSatjRINGLamc7KTERXDQ9k1V7a9hxsBHQacKVUsOPBoleVDe1kxIbzkXTMzAGnl+5D9ApOZRSw48GiW6MMVQ720iJCWdMWixTMuN5Y701hEODhFJquNEg0U1zu5vWDg8psREAfHFGJi0dbkAXHFJKDT8aJLqpcVrdX5NjwgG4cNqhMX1aklBKDTcaJLqparIG0qXGWkEiJzmamaMSAQ0SSqnhR0eHddM5kC4lJsK77boFo6lv7iA+Un9dSqnhRZ963XSvbgK4bGY2l83MDlSWlFIqYLS6qZsqe96mlNjwI6RUSqmhT4NENzVN7USHhxCtK9AppZQGie6qne1dqpqUUmo40yDRTVVTm3eMhFJKDXcaJLqpcbaTqiUJpZQCNEj0UN2k1U1KKdVJg4QP77xNWt2klFKABokuGttcdLgNKVqSUEopQINEF97R1jpGQimlAA0SXdR4B9JpdZNSSoEGiS6qvPM2aUlCKaVAg0QXWt2klFJdaZDw0VndpF1glVLKokHCR1VTO3ERoUSEhgQ6K0opNShokPBR42zXqiallPIRsCAhIueLyA4RKRSRew+T7koRMSJSMNB50oF0SinVVUCChIiEAA8DFwD5wNUikt9LujjgbmDF8ciXTsmhlFJdBaokMRcoNMYUG2PagReAS3pJ91PgV0Dr8chUtbPdu7a1UkqpwAWJLKDE532pvc1LRGYCOcaYtw53IhG5TURWi8jqysrKz50hj8dYbRIxWt2klFKdAhUkpJdtxrtTxAE8BPzXkU5kjHnMGFNgjClIS0v73Bmqb+nA7TFa3aSUUj4CFSRKgRyf99nAfp/3ccBU4CMR2QPMBxYOZON1tVMH0imlVHeBChKrgPEikici4cBVwMLOncaYemNMqjEm1xiTCywHLjbGrB6oDFU32fM2aXWTUkp5BSRIGGNcwF3AImAb8JIxZouIPCgiFwciTzVaklBKqR5CA/XBxpi3gbe7bbu/j7SnD3R+qjRIKKVUDzri2tZZ3ZQUrUFCKaU6aZCw1TjbSYwOIyxEfyVKKdVJn4i26qZ2XUdCKaW60SBhq3a2ac8mpZTqRoOErbpJZ4BVSqnuNEjYqnWacKWU6kGDBOD2GGqb20nW6iallOpCgwRQ29yOMegMsEop1Y0GCaz2CNC1rZVSqjsNEsCI+Ah+/5UTmDUqKdBZUUqpQSVg03IMJonR4Vw6M+vICZVSapjRkoRSSqk+aZBQSinVJw0SSiml+qRBQimlVJ80SCillOqTBgmllFJ90iChlFKqT2KMCXQe/EZEKoG9R3FIKlA1QNkJpKF6XaDXFoyG6nXB0Lm20caYtN52DKkgcbREZLUxpiDQ+fC3oXpdoNcWjIbqdcHQvrZOWt2klFKqTxoklFJK9Wm4B4nHAp2BATJUrwv02oLRUL0uGNrXBgzzNgmllFKHN9xLEkoppQ5Dg4RSSqk+DcsgISLni8gOESkUkXsDnZ/+EJEcEflQRLaJyBYR+Za9PVlEFovILvvfJHu7iMgf7WvcKCKzfM51vZ1+l4hcH6hr8iUiISKyTkTest/nicgKO48viki4vT3Cfl9o78/1Ocd99vYdInJeYK6kKxFJFJFXRGS7fe8WDIV7JiLfsf8ON4vI8yISGaz3TESeFJEKEdnss81v90hEZovIJvuYP4qIHN8rPEbGmGH1A4QARcAYIBzYAOQHOl/9yHcGMMt+HQfsBPKBXwH32tvvBX5pv/4C8A4gwHxghb09GSi2/02yXycNguv7LvAc8Jb9/iXgKvv1o8A37Nd3AI/ar68CXrRf59v3MgLIs+9xyCC4rn8At9ivw4HEYL9nQBawG4jyuVc3BOs9A04FZgGbfbb57R4BK4EF9jHvABcE+u/yqH4/gc5AAP4gFgCLfN7fB9wX6Hx9jut4AzgH2AFk2NsygB32678CV/uk32Hvvxr4q8/2LukCdC3ZwPvAmcBb9n+mKiC0+z0DFgEL7Nehdjrpfh990wXwuuLth6l02x7U98wOEiX2AzHUvmfnBfM9A3K7BQm/3CN733af7V3SBcPPcKxu6vwD71RqbwsadnF9JrACGGGMOQBg/5tuJ+vrOgfj9f8e+D7gsd+nAHXGGJf93jeP3vzb++vt9IPxusYAlcBTdlXa4yISQ5DfM2NMGfAbYB9wAOserGFo3LNO/rpHWfbr7tuDxnAMEr3VBwZNP2ARiQX+BXzbGNNwuKS9bDOH2R4QInIRUGGMWeO7uZek5gj7BtV12UKxqjEeMcbMBJxYVRd9CYprs+vnL8GqIsoEYoALekkajPfsSI72WoLxGrsYjkGiFMjxeZ8N7A9QXo6KiIRhBYhnjTGv2pvLRSTD3p8BVNjb+7rOwXb9JwEXi8ge4AWsKqffA4kiEmqn8c2jN//2/gSghsF3XWDlqdQYs8J+/wpW0Aj2e3Y2sNsYU2mM6QBeBU5kaNyzTv66R6X26+7bg8ZwDBKrgPF2T4xwrIa0hQHO0xHZPSKeALYZY37ns2sh0NmT4nqstorO7dfZvTHmA/V2sXkRcK6IJNnfCM+1twWEMeY+Y0y2MSYX6158YIy5BvgQuNJO1v26Oq/3Sju9sbdfZfekyQPGYzUYBowx5iBQIiIT7U1nAVsJ8nuGVc00X0Si7b/LzusK+nvmwy/3yN7XKCLz7d/VdT7nCg6BbhQJxA9WD4WdWL0pfhDo/PQzzydjFVM3Auvtny9g1e2+D+yy/0220wvwsH2Nm4ACn3PdBBTaPzcG+tp88nU6h3o3jcF6YBQCLwMR9vZI+32hvX+Mz/E/sK93B4OkBwlwArDavm+vY/V8Cfp7BvwE2A5sBp7B6qEUlPcMeB6rbaUD65v/zf68R0CB/XsqAv5Mt44Mg/1Hp+VQSinVp+FY3aSUUqqfNEgopZTqkwYJpZRSfdIgoZRSqk8aJJRSSvVJg4QKOiLiFpH1IrJBRNaKyIlHSJ8oInf047wficiQXtT+aInI30XkyiOnVEOVBgkVjFqMMScYY2ZgTRL38yOkT8SaiXRQ8hmlrNSgo0FCBbt4oBasea1E5H27dLFJRC6x0/wCGGuXPn5tp/2+nWaDiPzC53xfEpGVIrJTRE6x04aIyK9FZJW9hsDX7e0ZIvKJfd7Nnel9icgeEfmlfc6VIjLO3v53EfmdiHwI/NJev+B1+/zLRWS6zzU9Zed1o4hcYW8/V0SW2df6sj2nFyLyCxHZaqf9jb3tS3b+NojIJ0e4JhGRP9vn+DeHJrZTw5R+g1HBKEpE1mON5M3Amu8JoBW4zBjTICKpwHIRWYg1qd5UY8wJACJyAXApMM8Y0ywiyT7nDjXGzBWRLwA/xpqn6Gas6RfmiEgE8JmIvAdcjjX1ws9EJASI7iO/DfY5r8Oal+oie/sE4GxjjFtE/gSsM8ZcKiJnAk9jjdb+kf3Z0+y8J9nX9kP7WKeI/DfwXRH5M3AZMMkYY0Qk0f6c+4HzjDFlPtv6uqaZwERgGjACa7qNJ/t1V9SQpEFCBaMWnwf+AuBpEZmKNWXC/4nIqVjTjmdhPei6Oxt4yhjTDGCMqfHZ1zlx4hqsNQbAmodnuk/dfALWPEOrgCfFmnjxdWPM+j7y+7zPvw/5bH/ZGOO2X58MXGHn5wMRSRGRBDuvV3UeYIypFWvm3HysBztYixktAxqwAuXjdingLfuwz4C/i8hLPtfX1zWdCjxv52u/iHzQxzWpYUKDhApqxphl9jfrNKy5rNKA2caYDrFmlo3s5TCh7+ma2+x/3Rz6/yHAN40xPSbVswPShcAzIvJrY8zTvWWzj9fObnnq7bje8irAYmPM1b3kZy7WhHtXAXcBZxpjbheReXY+14vICX1dk12C0rl6lJe2SaigJiKTsJakrcb6NlxhB4gzgNF2skasJV87vQfcJCLR9jl8q5t6swj4hl1iQEQmiEiMiIy2P+9vWDP0zurj+K/4/LusjzSfANfY5z8dqDLWeiHvYT3sO683CVgOnOTTvhFt5ykWSDDGvA18G6u6ChEZa4xZYYy5H2tVuJy+rsnOx1V2m0UGcMYRfjdqiNOShApGnW0SYH0jvt6u138WeFNEVmPNkrsdwBhTLSKfibXQ/TvGmO/Z36ZXi0g78DbwP4f5vMexqp7WilW/U4nVpnE68D0R6QCasKaB7k2EiKzA+lLW49u/7QGsFew2As0cmqb6f4GH7by7gZ8YY14VkRuA5+32BLDaKBqBN0Qk0v69fMfe92sRGW9vex9rXemNfVzTa1htPJuwZkr++DC/FzUM6CywSg0gu8qrwBhTFei8KPV5aHWTUkqpPmlJQimlVJ+0JKGUUqpPGiSUUkr1SYOEUkqpPmmQUEop1ScNEkoppfqkQUIppVSfhtSI69TUVJObmxvobCilVFBZs2ZNlTEmrbd9QypI5Obmsnr16kBnQymlgoqI7O1rn1Y3KaWU6pMGCaWUUn3SIKGUUqpPGiSUUkr1SYOEUkqpPmmQUEop1ScNEmpYM8bw8IeFFFc2BTorSg1KGiTUsLazvIlfL9rBS6tLA50VpQYlDRJqWFuyqxJASxJK9WFIjbhWwaG1w80v391OXXMHd505jrFpsQHLyye7rKWni6ucAcuDUoPZgAcJETkf+AMQAjxujPlFt/2jgSeBNKAGuNYYU2rv+xVwIVaJZzHwLaPrrQ4KbS43/1i6h0tOyGJEfGS/j9tV3shdz61jR3kjkWEOFm7Yz5Wzsrn77PFkJUYNYI57au1ws3J3NQ6BvdVO3B5DiEOOax4UNLW5cHsMCVFhve6vaGwlOTqc0JDAVHy0udxsP9DIhtI6apzt3HhiHgnRvefVXzaX1fPsin3sr2uhvKGVgw2tuNyGKZnxnJCTyIycRFJjIzjY0Ep5fSvlDa3kpsZw7fzRfs/LgAYJEQkBHgbOAUqBVSKy0Biz1SfZb4CnjTH/EJEzgZ8DXxORE4GTgOl2uk+B04CPBjLPqn9+9e4Onvh0N8uLa3jyhjlHTG+M4aXVJfx44RZiwkP5+41zmJqVwF8+LOKfy/fy2royvnPOBG4/bQwix+dBvWZvLa0dHs7NH8F7W8sprW1mdErMcflsZVlaVMW3X1hPa4ebey+YzFVzcnDYgbq53cVDi3fyxKe7mZadyJ+umsmolOhj/szWDrf14K1vpbyxjfAQByMTIhkZH0lKbDh7q51sKKlnQ2kdG0rq2HagkXa3x3v8wg37eeL6OeSl+v9vxfeaY8JDyU2NITspmoLcJARhY1k9T322p0t+ACLDHFw4LTP4ggQwFyg0xhQDiMgLwCWAb5DIB75jv/4QeN1+bYBIIBwQIAwoH+D8DmlLi6r44Wubefn2BaTERnzu83ywvZwnPt3NmNQYPthewfvbyjlr8oge6YwxFFY0sWjLQd7dcpDNZQ2cODaF33/lBNLt0sf9X8zn5lPy+Nm/t/LLd7dT29zOfRdMOi6BYsmuKsJChK/OG8V7W8sprnRqkDhOXG4Pf3x/F3/6sJC81BjyUmP4n9c28dq6Uv7vsmmU1rbww9c3U1bXwoXTMvhkVyUX/nEJP79iGhdNz/xcn9nu8nDnc2tZvLV/j5GY8BCmZiVw48m5nJBtfXsvqWnm9n+u4dKHP+PRa2ezYGzKUefDGMO/1pbx0OKdxEWGeksGMRGh/PKd7ZTVtXD13FHce/6kXkssnSWb+pYORiZEMiI+kvjI0AH7PzPQQSILKPF5XwrM65ZmA3AFVpXUZUCciKQYY5aJyIfAAawg8WdjzLbuHyAitwG3AYwaNcr/VzCEvLnhAMVVTl5ZU8rXTxv7uc5R3tDKPS9vZHJGPC/fvoBLH/6Mn7y5lZPGpRIZFuJN98nOSh5YuMVb1z9zVCIPXjKFa+aN7lGlk5UYxZ+vnsUDsVt47JNimtpc/PSSqQNe9bNkVyUzRyUxLSsBsNolzhjQT1QAB+pb+Nbz61m5p4YrZ2fz4CVTiAoL4eU1pfzf29s4/w9LcHsM49JjeenrC5ibl0xJTTN3v7COu55bx2eFVTxw8RQiQkOO/GE2Yww/eG0Ti7eWc/PJeUwaGed9wLa7PByst6p0KhvbyE6K4oScRMakxfb4G8xMjOL1O0/ipr+v4mtPrOCOM8YRIsLBhlYqGloREWZkJzA9J5EZ2QkkRod3OX53lZMfvLaJpUXVnJCTSEJUGO9uOcgLq6zH5Lj0WF6+fQFzcpP7vJaI0BBm5CQexW/82Ax0kOjtf3n3NoV7gD+LyA3AJ0AZ4BKRccBkINtOt1hETjXGfNLlZMY8BjwGUFBQoO0Vh7GsyGqkfWFVCbedevTVOm6P4dsvrKel3c2frp5JbEQoD3xxCtc+sYLHPinm7rPGA/DOpgPc/cI6clNi+OmlUzk3f8QR2y0cDuEnF08hNiKUv3xUhLPNxXULRnOwvo2DDa20dri5eu4okmPCD3sesL6lOtvch603rmpqY8v+Bu45dwLJMeHER4YGVQ+n5nYXt/9zLU2tHXxtwWi+MC3jqB6aR9LmcuP2GKLD/fuIWF9Sx61Pr6a5zcVDX5nBZTOzvfu+XJDDmZPSeWjxTjISIrn11DHea8pJjualry/gd4t38shHRSRFh/P98yf1+3P//EEhL68p5e6zxvPdcyb02D/V/qLQH6NTYnj1jpO467m1/PH9XQAkx4TbAcfNf7YdKqmkxUUwMv7Qt/23Nh0gItTBzy6bytVzRuFwCMYY9tU0s7e6mXljkv16H/1hoINEKZDj8z4b2O+bwBizH7gcQERigSuMMfV2CWG5MabJ3vcOMB8rkKijVFbXwp7qZqZnJ7CxtJ7lxTVHVVSubmrjsSXFLCuu5ldXTmdcutUj6eTxqXxh2kge/rCQy2ZmsWJ3Dd9/ZQMzRyXx5A1z+myM7I2I8P3zJxEbGcqv3t3BG+u7/KmwtKiKZ26a562z7s7tMby+royH/rOT0toWshKj7KJ8AudPyehSn/1ZYZWd/zREhDFpsRRXBkcPp9YON7c9vYalRVXkJEfznRc38L9vbeOquTncdFLeMVUlAtS3dPDVvy2nuNLJpTOzuG7BaCZnxANQ62znP9vK+XBHBfUtHd5jwkIc3HRSHqdO6HXdGgDe3LCfe17eQHp8BM/dchLjR8T1SJMaG8HPLpvW6/FhIQ7++/xJlDe08viS3XypIKdf7QKvryvjt4t3cvnMLL5z9vgjpu+PhKgwnr5pLuUNbSTFhHV5sDe0drC5tJ71pXXsrWrmYEMrpbXNVDS2cW7+CO6/KN9b3QrW3/3olJhBW9U50EFiFTBeRPKwSghXAV/1TSAiqUCNMcYD3IfV0wlgH3CriPwcq0RyGvD7Ac5vUDPG8PLqUs7OH9HjG/eyomoAfnLxFK5/ciXPr9x32CDhbHPxxvr9fFZUxYaSOkprWwC49IRMvjQ7u0vaH1yYz4fbK7n+qZUUVzo5eVwqj103+3N/C73j9HHMzU2msdXlbVBctOUg9766iUc+LuLOM8b1uO5FW8r57Xs72FXRxNSseK6eO4qtBxrYUFLHvzcd4K8fF/PmN08m0+5BtWRXFQlRYd6qpjFpMSwtrP5c+T2cxVvLGZMW47duvh1uD3c9t5ZPC6v4zZdmcPnMLD4rquIfS/fyyEdFvLa2jCdumON9qB8tZ5uLG55ayc7yRs6bMpLX1pXy/Mp9zMlNItThYOWeGtweQ0ZCZJfeaHuqmrnuyZXcftpY/uvcCYT59ETyeAx//GAXv//PLubkJvHotbOPKZDde8EkFm8p54GFW/j7jXO6lIifWbaHRz8uJjE6jJHxkaTGRvDaujLm5SXz8yum+bXeXkQYmdCzhBwfGcaJ41I5cVyq3z4rkAY0SBhjXCJyF7AIqwvsk8aYLSLyILDaGLMQOB34uYgYrFLCnfbhrwBnApuwqqjeNca8OZD5DXa7q5x8/18bubk8jx9dlN9l39KiKpJjwpmRnchlM7N4fmUJtc52kroFk6LKJp5Ztpd/rSmlsc3l/TZ+3YLRTM9OZE5uco//aFmJUdx15jh+vWgH5+SP4E9Xz+zSPvF5FHSrk/3KnBw+Lazid4t3Mn9MCrNHJwFWCed7r2zkg+0VjEmL4S/XzOKCqSO75HH7wQaufGQZt/9zDS99fQERoQ4+3VXFyeNSvXXOY1JjeHVtGc42FzER/vlv0dzu4o5n13DelJH8+auzjvl8bo/huy9t4D/bKvjpJVO40g7Wp4xP45TxaWwqreeWp1dxxSNL+eNVMzk7/1BngsbWDnZXOZmamdBnSay1w80t/1jNxtJ6Hv7qLM6fOpK65nZeXl3K86v2EeoQvnHaWM6bMpKpWfFdfsetHW4efGsrj35cxPLiah76ygmU1bbw3taDvLelnIMNrVw+K4ufXz7tmKtT0uMi+fY5E/jpW1tZvLWcc6eMBOCFlfv40RtbmDXKquvfX9/KupI6pmTF89jXCgZdNU6wkKE07KCgoMAM5+VL3918kNv/uYbU2HCW/3q+NQAAIABJREFU33eWt1+5MYaTfvEBM0cl8fA1s9h+sIHzf7+EH144mVtOGQNY31B/8NomXlpdSliIcOG0DK47MZeZOYn9+vblcntYVlzN/DEpXb5F+lNDawcX/nEJHg+8/a1T2Lq/gW+/uI5aZwf3XjCJ6xaM7rMv/XtbDnLbM2v40uxsbjt1DOc89Ak/v3waV8+1Oju8vekAdzy7lre+efJR1U8fzic7K7nuyZVkJ0Xx6f+3d+fxcdXl4sc/T5bJvrVN27TpkkIXCpRutOxgRaWI7MjiVXBD3H7XDS1XLyheRa9cvVfhqlwEARUEROVisXIrmyzdKHRPG1qgbbokbZMm6SSZzDy/P853JieTSTstmWzzvF+vvHLO95wz/Z4cOM98928s6HY8HFEeW7mdS2eNPWJQbesI8/XH1/Dn12tZtHAaN/XQ8WB3YyuffnAl62obufkDUynNC/C3Dbt5uWYf7eEIl84cw51XndLt79TeEeEzD63kuc11/PjDXdsKjsZf1uxi0R/W0NTWAXhdM8+ZXM6HThnDRTMqeu2bfCgc4YM/fZFD7WH+7yvnsmT9br70+9c5d0o593x0LoEsm0ziaIjIKlWdm+iYjbgeQrbsaQKgvrmdF7fU855pIwF4e98hahtb+ZyrXpo22huQ88iK7XzyrCrawxG+8LvVPLNhD58+u4obzzmO8qKjqw7Iyszg7Mk910f3huLcbH56zSyu+sUrXPnzl6mpa6ZqeAH33XAqJ445/Iv9/SeO5osLjudnf6+hxjVQn+WrDphU7tUHb61vOaog0dzWQUtbR8KG+Ve2etVXOw4EqWtq6/Y3fa56L4ueWEtzW0csWCdS39zGZx5axaq3D3DzB6b2GCAARpfk8uhnTucrj77Ov/+1GoDxw/K5/owJZGQIv3x+K4faw/zsulmxb9abdh/klifWsvqdBr5/2cnHHCAAPjijghmVJTy6cjsnjS3hnMnl5AV6/xt8dmYG3774RK77n2V87rev8fzmOuZXDeMX/zTHAkQvsyAxhGze20xFSS7BUJgnVu+MBYmXXXvEGb42iOvmjefrf1jDC1vquffFrby4pd5rrzhjYn9kPWmzxpdx8wemcsfTm7hittd9MtnqoS+dP4W1Oxt5rrqOqhEFjBvW2ZA9cXgBIkc3h9Peplau/uWrBNvDvLRoQbfuki+/uY+inCya2jp4fXsD75vedSzJsm37AfjNq2/ziTOrElYDVe9u4pMPrKCuqY27rpuV1BiBvEAmd183m+c31zG6JJdpo4ti3+DHlORx25Pr+dQDK/mva2Zx74tbueeFrRTnZfPTa2dx8SnHNgbBb9ywfL76/qnv+nOO5IzjRnDRjAqeWrOLmeNKuff6U991NafpzoLEELJlTxMnVBQztjSPR1du52BriOLcbF5+s55RxTldeoJcdEoFtz+1gU89sIJwRPnRlTO4au64w3z6wHHjOZP44IwKKsuObvRtZobwX1fP4qpfvswFJ1V0OZabncmYkrykezgdaGnno/cuZ5sbB7J8W9feYgdbQ6zd0cCnzp7Er/6xjde3H0gYJHKyMnhr3yH+UVPfrWfQirf28/H7V5AXyOTRz5x+VH3jMzIk9iXB7/ozJpIfyOQbf1jDad9fSns4wpVzKvmXC09IqnvxQPPti09kUnkhnzyzisJeaksyXVm5bJBZt7ORmr1N3dI7whG21rUweWQhl88eS1tHhKfX7kJVeeXNfZxx3Igu9cH5gaxYw+dd180eNAECvF4lRxsgokrys1nypXMS9pWfVF4Qe+kfTlNriOvvX862fS3c+7G55GZn8PS6XV3OWb51PxGFBdNGckJFEavfaehyvLmtg3U7G7nhjIkMLwjw0Ktvdzne1hHm5sfeYHhhgD9//sxeHTx11dxx/PdHZjN7Qim/+9R87rzqlEEZIMDrMvuV901J+VxK6cyCxCDzpd+/zqI/rO2W/vb+Q7SHI0weVeSNFnW9dTbvaWZfS3vC7q7f/OAJvPj1BVx4ckW3Y0NZT42nx5UXsrWumcN15mhu6+ATv17BhtqD/Pwjszl/+ijeM3UkT6/bTSTSed3Lb+4jJyuDWeNLmTmulDU7Ggn7jr/29gHCEeWsySO4+tRxLN24h50Nwdjxe1/cxlv7DvHdS06KddvtTRecVMEjN54+ZLppmtSxIDHA7G9pj41piNcaCrO1rpk1Oxpp6wh3ORZttJ4yqhAR4fLZ3sC2x1Z6w/3PSBAkst3EZsZTNaKAlvYwe5vauh17q76F7z61gdPvWMqqtw/wn9fMjM1XtfDkCuqa2lj1zoHY+S+/Wc+pE73Rs7PGldHc1sGbvvaOZdv2kZUhzJlQxnXzx6PAw8veAbyBj3f9vYYLThx92MFpxvQFCxIDzD0vbOWffrWMptZQt2Nb9jQTUWgPR1i382CXY5v3eC+g6KCtS2eNBeD+l99i/LD8Y66eSSexHk6+dok9B1u54f7lnHfnczzw8lucN3Ukf/jsGV0akBdMG0kgK4PFa70qp33NbWza3RQrvc0c71UVrfYFkeXb9nPS2BLyA1lUluXz3mkjeWTFO7R3RPj+XzaiKN+66ISU37MxR2JBoo81BkN8+sGV7GoMJjy+eU8T4YiycVf3dodNuzsDw2tvH+hybMveZirL8mI9fSrL8jlt0jDCEU1YijDdTXIBdmt95zf+2/68nle37uPL50/h5UUL+Nm1s5g1vqzLdYU5WZw7pZy/uiqnV7d6vZaiQaJqeAHFuVm8vt1rl2gNhXljeyPzJ3UOGPyn0yZQ39zObU+u4y9rd/H58463wG4GBAsSfWz1Owd4ZsMent1Ul/D4FtcovW5nY7dj1bubyMnKYGxpHqvig8SeJqbEzYVzuevvfizTGaejiuJccrMzYiWJFzbX8df1u/nigsn88/mTu8y3E+/Ck0ezq7GV13c08MrWegpzspjhxltkZAgzx5fFGq9Xv9NAezjC/KrOIHHO5HLGD8vn4eXbGT8sn0+f0/O4CWP6kgWJPrarsRXwSgzxWkPh2BxJ62oTBIk9TUweVci8qmGseudArIE11rNpVNf5gS6dNZbvX3YyC09Kr4bpY5WRIUwc7vVwau+I8O3/Xc/E4fl86uyqI1773hNGkZ0pLF6zi5ff3Me8qmFdRjXPHFfK5j1NtLR1sGzbPkS6Tj2SkSF81C0Yc9uHplt/fzNgWJDoY7tcDxZ/1VHUm3XNqEIgM4MNtd2Pb9rdxNRRxcyeUEZdU1ssoMR6No3sWpIIZGVw3fzxNgL1KER7ON3/0ja21rVw24eSW7egODebsyeX88TqnWyta+lWxTdrfCkRhTU7Glm+bT/TK4opzu3abfPjZ07kz58/M+ECTsb0F3t79LFaV5Ko3t3UratlzV6vLvy8qeVs2dtMa6izB9O+5jbqmtqYNrqIOa5OPFrl5O/ZZN6dqhEFbD8Q5KdLt3D+CSMTDkjrycKTRrO/pR3oXsU3s9JrvF6+bT+vvXOAeVXdF5XJyszo08VkjEmGBYk+Fm2wPnAoRH1ze5djNXubyRC46JQxhCPKpt2dVVLVbntaRRFTRxdREMiMBYloz6boGg/m2E0qLyAcUUIR5daLTjyqa983fRRZGUJpfjYnjO46VXdZQYCJw/N5ePk7tIYizK+ydiIzOFiQ6GO7Glpjo1urd3dtl6jZ28yE4QXMct8m/Y3X0YAxdXQRmRnCrPFlviDRxLhheb2+ilg6ijb+33TOpC6LFCWjND/AVXPHcdWcyoTzMM0aX8bug15JMlFJwpiByIJEH1JVahuDnOsGSFXv6R4kjh9ZSGVZHiV52az3tUtU725iWEGAcrdYy+wJZWzafZDmtg5q9jZ3a48wx+bEMcX87tPzY0uxHq07Lj+Zb35wesJjM13wnzKqcNBOg2HSjwWJPtRwKERrKMKJY4oZURhgs68kEQpH2FbfwvEjvRHTJ40tZr2vh9OmPU1MHdU5m+ecCWVE1GuXSNSzyRwbEeGM40b0uC7FuxENElaKMIOJBYk+VOvaI8aU5jFlVBGbfCWJt/cdoiOiHO8GdJ00poRNu5oIhSNEIsqWPU1MHd1ZWpg5rhQR+ONrO2gPR5hiJYkBb/qYYi6bNZar547v76wYkzSrxO5Duxq8+uiKklymjCri0ZXbiUSUjAyJ9WyKlgimjymmPRxhy55mCnIyOdQeZpovSJTkZTNlZBGL1+3ucp0ZuLIzM/jJ1TP7OxvGHBUrSfShXb6SxNTRRRxqD8dm/oxO/hadeym6Otq62sYujdZ+syeU0d4RAaxnkzEmNSxI9KHaxlayMoQRhTmxF340AGzZ08SYktzY3EtVwwsoCGSyofYg1bubEKHbtBtzJnjjJaxnkzEmVSxI9KFdDUFGFeeSmSFMdt/8o9Nz1NQ1c7wvCGRkCNPHFLNuZyObdh9k/LD8bst0RoOEtUcYY1LFgkQfqm1sZUypN0lcUW42Y0vzqN7dRCSivLm3JdZoHXXimBI27DrIxl1ez6Z4E4fnM210kU3gZ4xJGauj6EO7GoPMGtc5zfS00UVU725iZ0OQYCjcrV3hxDHFHGoPs62+hQ/N6D5Jn4jw1y+dk/J8G2PSV8pLEiJygYhUi0iNiCxKcHyCiCwVkTUi8pyIVPqOjReRv4nIRhHZICITU53fVIlElN2NrVSUdk43PWV0EW/WNcfaJeKDRLTxGmBq3DQPxhjTF1IaJEQkE7gbWAhMB64VkfjhqHcCD6rqDOB24A7fsQeBH6nqCcA8YG8q85tK9S1thMLKmJLO9YqnjiqiI6I8s8F1Y40LEsePLIzN4Brfs8kYY/pCqksS84AaVd2qqu3AI8AlcedMB5a67Wejx10wyVLVZwBUtVlVD6U4vynjHyMRFe2ttGT9HoYXBCiLm6ohOzODE0YXEcjKYOJRziNkjDG9IdVBYiyw3be/w6X5vQFc4bYvA4pEZDgwBWgQkSdEZLWI/MiVTLoQkRtFZKWIrKyrS7zaW19bX9tIfXNblzT/GImo40YWkJkhNAZDHNfDOIdLZ43litmVKZkmwhhjjiTVb57uU2GCxu1/DThXRFYD5wI7gQ68RvWz3fFTgUnADd0+TPUeVZ2rqnPLy8t7MevH7mO/Ws73/rKxS1ptgpJETlYmVSMKgO5VTVEfP7OKOy4/OUU5NcaYw0t1kNgBjPPtVwK1/hNUtVZVL1fVWcA3XVqju3a1q6rqAP4EzE5xft+1g60h9rW08+KWui6LCu1qDJKTldFt9s9o11YbMW2MGYhSHSRWAJNFpEpEAsA1wJP+E0RkhIhE83ELcJ/v2jIRiRYPFgAbUpzfd22nW1K0vrm9y1TgtY2tVJTkxmZxjYo2SFuQMMYMRCkNEq4E8AVgCbAReFRV14vI7SJysTvtPKBaRDYDo4DvuWvDeFVNS0VkLV7V1f+kMr+9IRokAF6q2Rfb3tUQpMLXsynqvKnlTB1VxIyxtmylMWbgSflgOlVdDCyOS7vVt/048HgP1z4DzEhpBnvZjgNeB6xhBQFeqqnnk2dVAbCrsTXhyOgZlaUs+bINiDPGDEzWZaaX7Wzw2h4WnjSaZVv3EQpH6AhH2HOwtcsYCWOMGQwsSPSynQ1BxpblcfbkEbS0h3ljewN7m9qIKF1GWxtjzGBgQaKX7TwQZGxpHqdPGoEI/KOmvnOMhJUkjDGDjAWJXrbjQJDKsjxK8rM5eWwJL9XUd46RsJKEMWaQsSDRi4LtYfa1tDPWjao+8/gRrH6nIbY0aaLeTcYYM5BZkOhF0aVIK8u8eZbOOn4EHRHlyTdqKQhkUpxrM7MbYwYXCxK9KBokxpZ5JYY5E8oIZGWwrb6FitK8bgPpjDFmoLMg0YuiA+mi1U252ZmcOtFbZMg/Z5MxxgwWFiR60Y4Dh8jKEEYVdwaEM48fAVjPJmPM4GRBohftbAhSUZpLZkZntdJZLkhYzyZjzGCUVJAQkTtF5MRUZ2awi46R8DtpTAk3nXscF80Y00+5MsaYY5dsSWITcI+ILBORm0Sk5IhXpKGdDUHGlnZdQS4jQ1i0cJrN8mqMGZSSChKqeq+qngl8DJgIrBGR34nIe1KZucGkvSPC7oOtsZ5NxhgzFCTdJuGWDp3mfurxlh39iog8kqK8DSq7G1tRhcpSCxLGmKEjqdFdIvJj4GJgKfB9VV3uDv1QRKpTlbnBZEeDN0V4pZUkjDFDSLJDgNcB31LVQwmOzevF/AxasTESFiSMMUNIstVNB4Ds6I6IlIrIpRBbjzqt/ObVt/n8b1/rkrbjQBARm5/JGDO0JBskbvMHA1VtAG5LTZYGvhVv7ecva3exbmdnfNzZEGRkUQ6BLBt6YowZOpJ9oyU6L21nqwu2hwF4fNWOWNrOA8HYxH7GGDNUJBskVorIj0XkOBGZJCI/AValMmMDWWtHBIA/rt5Ja8gLGN4YCatqMsYMLckGiS8C7cDvgceAVuDzqcrUQNfaHiYvO5PGYIj/27iHcETZ1Ri0RmtjzJCTVJWRqrYAi1Kcl0GjtSPMqVXDqNnTxGMrdzBnQhmhsFpJwhgz5CQ7TqIc+DpwIhCbqU5VF6QoXwNasD3M2NJMrphTyV3P1rDyrQOAjZEwxgw9yVY3/RZv/qYq4DvAW8CKZC4UkQtEpFpEakSkW2lERCaIyFIRWSMiz4lIZdzxYhHZKSJ3JZnXlGvtCJObncmVcypRhbv+XgNYkDDGDD3JBonhqvorIKSqz6vqJ4DTjnSRm8rjbmAhMB24VkSmx512J/Cgqs4AbgfuiDv+XeD5JPPZJ4LtEXKzM5kwvID5VcOo3tMEwBirbjLGDDHJBomQ+71LRD4oIrOAysNd4MwDalR1q6q2A48Al8SdMx1vug+AZ/3HRWQOMAr4W5L57BNtoTC52d6f7sNzxwEwrCBAfiBtewUbY4aoZIPEv7npwb8KfA24F/hyEteNBbb79ne4NL83gCvc9mVAkYgMF5EM4D+Amw/3D4jIjSKyUkRW1tXVJZGldy8Y8no3ASw8eTSFOVlW1WSMGZKO+NXXVRlNVtWngEbgaKYHlwRpGrf/NeAuEbkBeAHYCXQAnwMWq+p2kUQf4z5M9R7gHoC5c+fGf3avC4UjdESUXBck8gNZfPviE2MlC2OMGUqOGCRUNSwiFwM/OYbP3wGM8+1XArVxn18LXA4gIoXAFaraKCKnA2eLyOeAQiAgIs2q2q9dcaOD56IlCYAr5yRT82aMMYNPspXoL7veRb8HWqKJqvpaz5cAXg+oySJShVdCuAa4zn+CiIwA9qtqBLgFuM999kd859wAzO3vAAHQGvJGW1vJwRiTDpINEme437f70hQ47DgJVe0QkS8AS4BM4D5VXS8itwMrVfVJ4DzgDhFRvOqmAT2SO1qSyPWVJIwxZqhKdsT1MS9TqqqLgcVxabf6th8HHj/CZ/wa+PWx5qE3WZAwxqSTZEdc35ooXVVvT5Q+lAUTtEkYY8xQlWx1U4tvOxe4CNjY+9kZ+DrbJCxIGGOGvmSrm/7Dvy8idwJPpiRHA1ysJBGwhmtjzNB3rG+6fGBSb2ZksIi2SeRkWUnCGDP0JdsmsZbOQXCZQDldezqljdg4iYAFCWPM0Jdsm8RFvu0OYI+qdqQgPwOe9W4yxqSTZKubKvAGvL2tqjuBXBGZn8J8DVjR9a1zs6xNwhgz9CX7pvs50OzbP+TS0k50fWurbjLGpINkg4SoamzyPDeFRlrOi91ZkrAgYYwZ+pINEltF5P+JSLb7+WdgayozNlC1doQJZGWQkdHzzLTGGDNUJBskbsKbv2kn3syu84EbU5Wpgay1PWztEcaYtJHsYLq9eDO4pr3WUMTaI4wxaSOpr8Qi8oCIlPr2y0TkvtRla+AKhsLW/dUYkzaSrTeZoaoN0R1VPQDMSk2WBrZW39Klxhgz1CUbJDJEpCy6IyLDSNfeTaEwORYkjDFpItkX/X/grU4XXffhKuB7qcnSwNYWipBnq9IZY9JEsg3XD4rIKuA9gACXq+qGlOZsgGrtCDOsINDf2TDGmD6RdJWRW3a0Dm89CURkvKq+k7KcDVDB9jB5pVbdZIxJD8n2brpYRLYA24DngbeAp1OYrwGrtcN6Nxlj0keylevfBU4DNqtqFfBe4KWU5WoAC7ZHLEgYY9JGskEipKr78Ho5Zajqs8DMFOZrwGoLhcm1hmtjTJpItk2iQUQKgReA34rIXrx1JdJO0MZJGGPSSLJfiS/Bmx78y8BfgTeBDyVzoYhcICLVIlIjIosSHJ8gIktFZI2IPCcilS59poi8IiLr3bGrk8xryoTCEToiatVNxpi0kWwX2Ba3GQEeiD8uIq+o6ukJ0jOBu4H34U0MuEJEnozrPnsn8KCqPiAiC4A7gI/iBaWPqeoWERkDrBKRJf6R330ttnSpBQljTJrorcr13B7S5wE1qrpVVduBR/BKJX7TgaVu+9nocVXdrKpb3HYtsBdvbe1+0xryFhyyNgljTLrorbed9pA+Ftju29/h0vzeAK5w25cBRSIy3H+CiMwDAnjVXMQdu1FEVorIyrq6umPJe9JsfWtjTLpJ9VfiRCvzxAeUrwHnishq4Fy8NStijeIiUgE8BHzcrYjX9cNU71HVuao6t7w8tQUNCxLGmHTTW5P09bRM2w5gnG+/Eqj1n+Cqki4HcD2orlDVRrdfDPwF+JaqvtpLeT1mQWuTMMakmWRHXC9MkHaTb/ejPVy6ApgsIlUiEsBbuOjJuM8ZISLRfNwC3OfSA8Af8Rq1H0smn6nW2SZhQcIYkx6SrW76V9fzCAAR+Qa+BmhVXZfoIlXtAL4ALAE2Ao+6OaBuF5GL3WnnAdUishkYRefssh8GzgFuEJHX3U+/DuCLlSQC1nBtjEkPyVY3XQw8JSI3AxcA01zaEanqYmBxXNqtvu3HgccTXPcb4DdJ5q9PRNskcrKsJGGMSQ/JjpOod9/8/w9YBVypqj31aBqyrOHaGJNuDhskRKSJrr2RAsAk4EoRUVUtTmXmBprYYLqABQljTHo4bJBQ1aJkPkRETlTV9b2TpYEr2O5KElnWJmGMSQ+99bZ7qJc+Z0Br7fB6N1lJwhiTLnorSPQ0TmJI6SxJWJAwxqSHVE/LMaS0doQJZGWQkZEWMdEYY1I+LceQ0toetvYIY0xa6a03Xnsvfc6A1hqKWHuEMSatJD13k4hcDpyFV7X0D1X9Y/SYqp6WgrwNOMFQ2MZIGGPSSrJzN/03cBOwFlgHfEZE7k5lxgaiVlu61BiTZpItSZwLnBQdZS0iD+AFjLQSDIXJsSBhjEkjybZJVAPjffvjgDW9n52BrS0UIc9WpTPGpJFkSxLDgY0istztnwq8IiJPAqhqUpP9DXbBUJjhhYH+zoYxxvSZZIPErUc+ZeizNgljTLpJdhbY50VkFF4JAmC5qu5NXbYGJuvdZIxJN8n2bvowsBy4Cm8xoGUicmUqM9afNtQe5Mwf/J365rYu6a2hiAUJY0xaSba66ZvAqdHSg4iU460t0W2xoKFgXW0jOxuCbNnTzIjCnFh6ayhMrjVcG2PSSLJvvIy46qV9R3HtoHMwGAJIUJKwNgljTHpJtiTxtIgsAR52+1cTtyTpUNLogkRdU2eQCIUjdETUqpuMMWkl2dKAAr8EZgCnAPekLEcDQMOh7iWJ2Kp0FiSMMWkk2ZLE+1T1G8AT0QQR+Q7wjZTkqp8lKkm0hrwFh6xNwhiTTo60xvVngc8Bk0TEP8K6CHgplRnrT40J2iSiJQmrbjLGpJMjlSR+BzwN3AEs8qU3qer+lOWqn3UGic4Z0C1IGGPS0WHrTlS1UVXfUtVrVfVt30/SAUJELhCRahGpEZFFCY5PEJGlIrJGRJ4TkUrfsetFZIv7uf7obu3YJapuClqQMMakoZRWsItIJnA3sBCYDlwrItPjTrsTeFBVZwC345VaEJFhwG3AfGAecJuIlKUyv1HRILGvpY1IxFuZNdomYQ3Xxph0kupW2HlAjapuVdV24BHgkrhzpgNL3fazvuMfAJ5R1f2qegB4BrggxflFVWkMhsgPZBIKayxgdJYkrOHaGJM+Uv3GGwts9+3vcGl+bwBXuO3LgCIRGZ7ktYjIjSKyUkRW1tXVvesMN7d1EI4ox5UXAp2N19YmYYxJR6kOEpIgTeP2vwacKyKr8RY32gl0JHktqnqPqs5V1bnl5eXvNr+xksPxI70gUWdBwhiTxpJe4/oY7cBboCiqEqj1n6CqtcDlACJSCFyhqo0isgM4L+7a51KZWUgQJJq6Bom8gAUJY0z6SHVJYgUwWUSqRCQAXAM86T9BREaISDQftwD3ue0lwPtFpMw1WL/fpaVUNEgcV14AdHaDDba7kkSWtUkYY9JHSt94qtoBfAHv5b4ReFRV14vI7SISXc3uPKBaRDYDo4DvuWv3A9/FCzQrgNv7YmxGo5uSY9ywfLIzpbMk0eF6N1lJwhiTRlJd3YSqLiZuMkBVvdW3/Tg9TDmuqvfRWbLoE9GSRFl+gBGFObGG686ShAUJY0z6sLqTONEgUZKX3SVItHaECWRlkJGRqD3dGGOGJgsScRqCIbIyhPxAJiMKA53VTe1ha48wxqQde+vFaQyGKM3PRkQoL/KVJEIRa48wxqQdCxJxGoMhivOyAVx1UzuRiBIMhW2MhDEm7ViQiHMwGKLEBYnyohzCEaUhGLKlS40xacmCRJyGQ51BYkRhDuBNzREMhcmxIGGMSTMWJOI0BkOU+koS4I26bgtFyLPJ/YwxacbeenEagz2XJKxNwhiTbixI+EQiysFWX5tEYWdJwtokjDHpyIKET1NrB6rEejcV52URyMygzkoSxpg0ZUHCJzraujQ/AICIxAbUtYYiFiSMMWnHgoSPf0qOKG9AXTutobCtSmeMSTv21vNpCHrTgvuDxIjCHOqtTcIYk6YsSPh0Vjd1LUnsPthKR0StuskYk3YsSPgkqm4aUZhSQwpaAAAMP0lEQVTD/havhGHVTcaYdGNvPZ/EQSIQ27bqJmNMurEg4dN4KEQgK6NLtVJ5UW5s26blMMakGwsSPv4pOaKsJGGMSWcWJHz8U3JERedvAqzh2hiTdixI+PhngI0a4QsSVpIwxqQbCxI+iUoSRTlZ5LhlS613kzEm3dhbz6cxGKIkv2uQ8Kbm8EoTVt1kjEk3FiR8DiYoSUBnlZMFCWNMukl5kBCRC0SkWkRqRGRRguPjReRZEVktImtE5EKXni0iD4jIWhHZKCK3pDKfHeEITW0dCYNEdMrwvIAFCWNMeklpkBCRTOBuYCEwHbhWRKbHnfYt4FFVnQVcA/y3S78KyFHVk4E5wGdEZGKq8nqwtQOgWxdYgPIirxtsbpYVvIwx6SXVb715QI2qblXVduAR4JK4cxQodtslQK0vvUBEsoA8oB04mKqMxkZb51tJwhhjorJS/Pljge2+/R3A/Lhzvg38TUS+CBQA57v0x/ECyi4gH/iyqu6P/wdE5EbgRoDx48cfc0YTTckR9eFTxzG6JI/8QKr/XMYYM7CkuiQhCdI0bv9a4NeqWglcCDwkIhl4pZAwMAaoAr4qIpO6fZjqPao6V1XnlpeXH3NGGw51nyY8qrIsn+vmH3sAMsaYwSrVQWIHMM63X0lndVLUJ4FHAVT1FSAXGAFcB/xVVUOquhd4CZibqox2liQCRzjTGGPSR6qDxApgsohUiUgAr2H6ybhz3gHeCyAiJ+AFiTqXvkA8BcBpwKZUZfTgYaqbjDEmXaU0SKhqB/AFYAmwEa8X03oRuV1ELnanfRX4tIi8ATwM3KCqitcrqhBYhxds7lfVNanKa8MhCxLGGBMv5S2xqroYWByXdqtvewNwZoLrmvG6wfaJxmCIvOxMAtbN1RhjYuyN6DQGQ12WLTXGGGNBIibR5H7GGJPuLEg4DcEQxRYkjDGmCwsSzsEEq9IZY0y6syDhWHWTMcZ0Z0HCSbQqnTHGpDsLEkB7R4RgKGxBwhhj4liQoHNKDusCa4wxXVmQoDNIWO8mY4zpyoIEUJCTySfOrGLKqKL+zooxxgwotkACUFGSx60fil8wzxhjjJUkjDHG9MiChDHGmB5ZkDDGGNMjCxLGGGN6ZEHCGGNMjyxIGGOM6ZEFCWOMMT0SbznpoUFE6oC3j+KSEUB9irLTn4bqfYHd22A0VO8Lhs69TVDV8kQHhlSQOFoislJV5/Z3PnrbUL0vsHsbjIbqfcHQvrcoq24yxhjTIwsSxhhjepTuQeKe/s5AigzV+wK7t8FoqN4XDO17A9K8TcIYY8zhpXtJwhhjzGFYkDDGGNOjtAwSInKBiFSLSI2ILOrv/CRDRMaJyLMislFE1ovIP7v0YSLyjIhscb/LXLqIyE/dPa4Rkdm+z7renb9FRK7vr3vyE5FMEVktIk+5/SoRWeby+HsRCbj0HLdf445P9H3GLS69WkQ+0D930pWIlIrI4yKyyT2704fCMxORL7v/DteJyMMikjtYn5mI3Ccie0VknS+t156RiMwRkbXump+KiPTtHb5LqppWP0Am8CYwCQgAbwDT+ztfSeS7ApjttouAzcB04N+BRS59EfBDt30h8DQgwGnAMpc+DNjqfpe57bIBcH9fAX4HPOX2HwWucdu/AD7rtj8H/MJtXwP83m1Pd88yB6hyzzhzANzXA8Cn3HYAKB3szwwYC2wD8nzP6obB+syAc4DZwDpfWq89I2A5cLq75mlgYX//d3lUf5/+zkA//AdxOrDEt38LcEt/5+sY7uPPwPuAaqDCpVUA1W77l8C1vvOr3fFrgV/60ruc10/3UgksBRYAT7n/meqBrPhnBiwBTnfbWe48iX+O/vP68b6K3ctU4tIH9TNzQWK7eyFmuWf2gcH8zICJcUGiV56RO7bJl97lvMHwk47VTdH/wKN2uLRBwxXXZwHLgFGqugvA/R7pTuvpPgfi/f8n8HUg4vaHAw2q2uH2/XmM5d8db3TnD8T7mgTUAfe7qrR7RaSAQf7MVHUncCfwDrAL7xmsYmg8s6jeekZj3XZ8+qCRjkEiUX3goOkHLCKFwB+AL6nqwcOdmiBND5PeL0TkImCvqq7yJyc4VY9wbEDdl5OFV43xc1WdBbTgVV30ZFDcm6ufvwSvimgMUAAsTHDqYHxmR3K09zIY77GLdAwSO4Bxvv1KoLaf8nJURCQbL0D8VlWfcMl7RKTCHa8A9rr0nu5zoN3/mcDFIvIW8AheldN/AqUikuXO8ecxln93vATYz8C7L/DytENVl7n9x/GCxmB/ZucD21S1TlVDwBPAGQyNZxbVW89oh9uOTx800jFIrAAmu54YAbyGtCf7OU9H5HpE/ArYqKo/9h16Eoj2pLger60imv4x1xvjNKDRFZuXAO8XkTL3jfD9Lq1fqOotqlqpqhPxnsXfVfUjwLPAle60+PuK3u+V7nx16de4njRVwGS8BsN+o6q7ge0iMtUlvRfYwCB/ZnjVTKeJSL777zJ6X4P+mfn0yjNyx5pE5DT3t/qY77MGh/5uFOmPH7weCpvxelN8s7/zk2Sez8Irpq4BXnc/F+LV7S4Ftrjfw9z5Atzt7nEtMNf3WZ8AatzPx/v73nz5Oo/O3k2T8F4YNcBjQI5Lz3X7Ne74JN/133T3W80A6UECzARWuuf2J7yeL4P+mQHfATYB64CH8HooDcpnBjyM17YSwvvm/8nefEbAXPd3ehO4i7iODAP9x6blMMYY06N0rG4yxhiTJAsSxhhjemRBwhhjTI8sSBhjjOmRBQljjDE9siBhBh0RCYvI6yLyhoi8JiJnHOH8UhH5XBKf+5yIDOlF7Y+WiPxaRK488plmqLIgYQajoKrOVNVT8CaJu+MI55fizUQ6IPlGKRsz4FiQMINdMXAAvHmtRGSpK12sFZFL3Dk/AI5zpY8fuXO/7s55Q0R+4Pu8q0RkuYhsFpGz3bmZIvIjEVnh1hD4jEuvEJEX3Oeui57vJyJvicgP3WcuF5HjXfqvReTHIvIs8EO3fsGf3Oe/KiIzfPd0v8vrGhG5wqW/X0Recff6mJvTCxH5gYhscOfe6dKucvl7Q0ReOMI9iYjc5T7jL3RObGfSlH2DMYNRnoi8jjeStwJvvieAVuAyVT0oIiOAV0XkSbxJ9U5S1ZkAIrIQuBSYr6qHRGSY77OzVHWeiFwI3IY3T9En8aZfOFVEcoCXRORvwOV4Uy98T0Qygfwe8nvQfebH8OalusilTwHOV9WwiPwMWK2ql4rIAuBBvNHa/+r+7ZNd3svcvX3LXdsiIt8AviIidwGXAdNUVUWk1P07twIfUNWdvrSe7mkWMBU4GRiFN93GfUk9FTMkWZAwg1HQ98I/HXhQRE7CmzLh+yJyDt6042PxXnTxzgfuV9VDAKq633csOnHiKrw1BsCbh2eGr26+BG+eoRXAfeJNvPgnVX29h/w+7Pv9E1/6Y6oadttnAVe4/PxdRIaLSInL6zXRC1T1gHgz507He7GDt5jRK8BBvEB5rysFPOUuewn4tYg86ru/nu7pHOBhl69aEfl7D/dk0oQFCTOoqeor7pt1Od5cVuXAHFUNiTezbG6Cy4Sep2tuc7/DdP7/IcAXVbXbpHouIH0QeEhEfqSqDybKZg/bLXF5SnRdorwK8IyqXpsgP/PwJty7BvgCsEBVbxKR+S6fr4vIzJ7uyZWgbK4eE2NtEmZQE5FpeEvS7sP7NrzXBYj3ABPcaU14S75G/Q34hIjku8/wVzclsgT4rCsxICJTRKRARCa4f+9/8Gbond3D9Vf7fr/SwzkvAB9xn38eUK/eeiF/w3vZR++3DHgVONPXvpHv8lQIlKjqYuBLeNVViMhxqrpMVW/FWxVuXE/35PJxjWuzqADec4S/jRnirCRhBqNomwR434ivd/X6vwX+V0RW4s2SuwlAVfeJyEviLXT/tKre7L5NrxSRdmAx8C+H+ffuxat6ek28+p06vDaN84CbRSQENONNA51Ijogsw/tS1u3bv/NtvBXs1gCH6Jym+t+Au13ew8B3VPUJEbkBeNi1J4DXRtEE/FlEct3f5cvu2I9EZLJLW4q3rvSaHu7pj3htPGvxZkp+/jB/F5MGbBZYY1LIVXnNVdX6/s6LMcfCqpuMMcb0yEoSxhhjemQlCWOMMT2yIGGMMaZHFiSMMcb0yIKEMcaYHlmQMMYY06P/D6Q9HaWhIEm9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.878086, 0.875541, 0.876050, 0.874777])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8761135, 0.0012256403428412476)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## start 0.3 8763"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixup = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_pct = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "moms = (0.95, 0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.082530</td>\n",
       "      <td>1.994339</td>\n",
       "      <td>0.357088</td>\n",
       "      <td>0.838890</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.905077</td>\n",
       "      <td>1.724474</td>\n",
       "      <td>0.462968</td>\n",
       "      <td>0.907356</td>\n",
       "      <td>00:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.757511</td>\n",
       "      <td>1.756677</td>\n",
       "      <td>0.468567</td>\n",
       "      <td>0.910919</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.634870</td>\n",
       "      <td>1.404287</td>\n",
       "      <td>0.618478</td>\n",
       "      <td>0.942225</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.553471</td>\n",
       "      <td>1.355133</td>\n",
       "      <td>0.635531</td>\n",
       "      <td>0.948078</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.489457</td>\n",
       "      <td>1.228751</td>\n",
       "      <td>0.699415</td>\n",
       "      <td>0.958768</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.438159</td>\n",
       "      <td>1.311792</td>\n",
       "      <td>0.638839</td>\n",
       "      <td>0.960295</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.365467</td>\n",
       "      <td>1.123640</td>\n",
       "      <td>0.750064</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.338461</td>\n",
       "      <td>1.292718</td>\n",
       "      <td>0.669636</td>\n",
       "      <td>0.945024</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.302479</td>\n",
       "      <td>1.087006</td>\n",
       "      <td>0.759735</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.274280</td>\n",
       "      <td>1.214529</td>\n",
       "      <td>0.688216</td>\n",
       "      <td>0.964368</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.251746</td>\n",
       "      <td>1.024358</td>\n",
       "      <td>0.789768</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.225921</td>\n",
       "      <td>1.149105</td>\n",
       "      <td>0.730211</td>\n",
       "      <td>0.963604</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.187177</td>\n",
       "      <td>0.985009</td>\n",
       "      <td>0.802240</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.168915</td>\n",
       "      <td>1.134929</td>\n",
       "      <td>0.736829</td>\n",
       "      <td>0.965895</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.145680</td>\n",
       "      <td>0.966237</td>\n",
       "      <td>0.814202</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.119823</td>\n",
       "      <td>1.004923</td>\n",
       "      <td>0.803003</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.118776</td>\n",
       "      <td>0.929673</td>\n",
       "      <td>0.831509</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.093290</td>\n",
       "      <td>1.021571</td>\n",
       "      <td>0.793077</td>\n",
       "      <td>0.970476</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.079356</td>\n",
       "      <td>0.942956</td>\n",
       "      <td>0.824637</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.069096</td>\n",
       "      <td>1.082392</td>\n",
       "      <td>0.769407</td>\n",
       "      <td>0.970221</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.059348</td>\n",
       "      <td>0.908684</td>\n",
       "      <td>0.832273</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.045078</td>\n",
       "      <td>0.986560</td>\n",
       "      <td>0.801476</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.030374</td>\n",
       "      <td>0.913664</td>\n",
       "      <td>0.829219</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.051506</td>\n",
       "      <td>1.076552</td>\n",
       "      <td>0.761262</td>\n",
       "      <td>0.966404</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.037646</td>\n",
       "      <td>0.931639</td>\n",
       "      <td>0.831764</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.032413</td>\n",
       "      <td>0.936057</td>\n",
       "      <td>0.831764</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.021784</td>\n",
       "      <td>0.933198</td>\n",
       "      <td>0.825401</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.012887</td>\n",
       "      <td>0.968325</td>\n",
       "      <td>0.809621</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.992299</td>\n",
       "      <td>0.916820</td>\n",
       "      <td>0.838636</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.994348</td>\n",
       "      <td>1.012686</td>\n",
       "      <td>0.802749</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.993310</td>\n",
       "      <td>0.909264</td>\n",
       "      <td>0.836600</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.971716</td>\n",
       "      <td>1.001817</td>\n",
       "      <td>0.808603</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.957215</td>\n",
       "      <td>0.891682</td>\n",
       "      <td>0.848562</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.954044</td>\n",
       "      <td>0.958773</td>\n",
       "      <td>0.819292</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.950189</td>\n",
       "      <td>0.873141</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.933393</td>\n",
       "      <td>0.954304</td>\n",
       "      <td>0.818274</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.940260</td>\n",
       "      <td>0.901510</td>\n",
       "      <td>0.838636</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.920225</td>\n",
       "      <td>0.917382</td>\n",
       "      <td>0.833800</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.935795</td>\n",
       "      <td>0.881358</td>\n",
       "      <td>0.854925</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.905730</td>\n",
       "      <td>0.934673</td>\n",
       "      <td>0.836091</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.915727</td>\n",
       "      <td>0.883425</td>\n",
       "      <td>0.851616</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.892844</td>\n",
       "      <td>1.043687</td>\n",
       "      <td>0.795113</td>\n",
       "      <td>0.964368</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.905192</td>\n",
       "      <td>0.870962</td>\n",
       "      <td>0.857216</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.886938</td>\n",
       "      <td>0.899738</td>\n",
       "      <td>0.845508</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.882212</td>\n",
       "      <td>0.881572</td>\n",
       "      <td>0.848562</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.880951</td>\n",
       "      <td>0.894279</td>\n",
       "      <td>0.844999</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.881837</td>\n",
       "      <td>0.884206</td>\n",
       "      <td>0.849071</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.885787</td>\n",
       "      <td>0.922927</td>\n",
       "      <td>0.831509</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.875925</td>\n",
       "      <td>0.873162</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.855106</td>\n",
       "      <td>0.886415</td>\n",
       "      <td>0.846271</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.854870</td>\n",
       "      <td>0.876311</td>\n",
       "      <td>0.859761</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.858805</td>\n",
       "      <td>0.868468</td>\n",
       "      <td>0.850598</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.861072</td>\n",
       "      <td>0.850511</td>\n",
       "      <td>0.862815</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.849742</td>\n",
       "      <td>0.856458</td>\n",
       "      <td>0.866378</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.832439</td>\n",
       "      <td>0.849729</td>\n",
       "      <td>0.859761</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.848272</td>\n",
       "      <td>0.870057</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.829152</td>\n",
       "      <td>0.854027</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.850383</td>\n",
       "      <td>0.854107</td>\n",
       "      <td>0.864597</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.831056</td>\n",
       "      <td>0.835478</td>\n",
       "      <td>0.871214</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.827139</td>\n",
       "      <td>0.850673</td>\n",
       "      <td>0.863578</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.827628</td>\n",
       "      <td>0.836439</td>\n",
       "      <td>0.868923</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.829053</td>\n",
       "      <td>0.835657</td>\n",
       "      <td>0.869178</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.821112</td>\n",
       "      <td>0.840515</td>\n",
       "      <td>0.863324</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.822717</td>\n",
       "      <td>0.835087</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.823690</td>\n",
       "      <td>0.830046</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.813533</td>\n",
       "      <td>0.826260</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.825057</td>\n",
       "      <td>0.827258</td>\n",
       "      <td>0.871723</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.820572</td>\n",
       "      <td>0.827165</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.818380</td>\n",
       "      <td>0.824024</td>\n",
       "      <td>0.871723</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.817377</td>\n",
       "      <td>0.826832</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.808434</td>\n",
       "      <td>0.821070</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.802077</td>\n",
       "      <td>0.820594</td>\n",
       "      <td>0.872741</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.812373</td>\n",
       "      <td>0.820396</td>\n",
       "      <td>0.875286</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.822392</td>\n",
       "      <td>0.821133</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.810232</td>\n",
       "      <td>0.821148</td>\n",
       "      <td>0.872487</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.805512</td>\n",
       "      <td>0.820952</td>\n",
       "      <td>0.873505</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.815274</td>\n",
       "      <td>0.820961</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.811648</td>\n",
       "      <td>0.818775</td>\n",
       "      <td>0.874777</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.818688</td>\n",
       "      <td>0.820813</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "moms = (0.95, 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.089985</td>\n",
       "      <td>1.947761</td>\n",
       "      <td>0.370069</td>\n",
       "      <td>0.831764</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.867596</td>\n",
       "      <td>1.602813</td>\n",
       "      <td>0.515398</td>\n",
       "      <td>0.919827</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.746143</td>\n",
       "      <td>1.693024</td>\n",
       "      <td>0.507763</td>\n",
       "      <td>0.900993</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.650589</td>\n",
       "      <td>1.373004</td>\n",
       "      <td>0.634258</td>\n",
       "      <td>0.954187</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.543426</td>\n",
       "      <td>1.642720</td>\n",
       "      <td>0.524561</td>\n",
       "      <td>0.923645</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.477509</td>\n",
       "      <td>1.175826</td>\n",
       "      <td>0.726139</td>\n",
       "      <td>0.962586</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.424616</td>\n",
       "      <td>1.327122</td>\n",
       "      <td>0.656401</td>\n",
       "      <td>0.951387</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.372196</td>\n",
       "      <td>1.122791</td>\n",
       "      <td>0.753118</td>\n",
       "      <td>0.965640</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.343350</td>\n",
       "      <td>1.158331</td>\n",
       "      <td>0.725630</td>\n",
       "      <td>0.966658</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.306418</td>\n",
       "      <td>1.077330</td>\n",
       "      <td>0.767625</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.280106</td>\n",
       "      <td>1.117961</td>\n",
       "      <td>0.745482</td>\n",
       "      <td>0.966404</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.243052</td>\n",
       "      <td>1.008397</td>\n",
       "      <td>0.792314</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.205606</td>\n",
       "      <td>1.111027</td>\n",
       "      <td>0.754645</td>\n",
       "      <td>0.966149</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.195012</td>\n",
       "      <td>0.976654</td>\n",
       "      <td>0.805548</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.176542</td>\n",
       "      <td>1.091943</td>\n",
       "      <td>0.761008</td>\n",
       "      <td>0.968694</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.145446</td>\n",
       "      <td>0.977122</td>\n",
       "      <td>0.802749</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.133089</td>\n",
       "      <td>1.081368</td>\n",
       "      <td>0.761517</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.107946</td>\n",
       "      <td>0.963941</td>\n",
       "      <td>0.813948</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.101042</td>\n",
       "      <td>1.054032</td>\n",
       "      <td>0.777297</td>\n",
       "      <td>0.971494</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.081916</td>\n",
       "      <td>0.934214</td>\n",
       "      <td>0.821838</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.058900</td>\n",
       "      <td>0.980524</td>\n",
       "      <td>0.804785</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.062526</td>\n",
       "      <td>0.952877</td>\n",
       "      <td>0.818529</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.042322</td>\n",
       "      <td>0.965337</td>\n",
       "      <td>0.817765</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.042719</td>\n",
       "      <td>0.914096</td>\n",
       "      <td>0.831509</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.008530</td>\n",
       "      <td>1.011887</td>\n",
       "      <td>0.789259</td>\n",
       "      <td>0.968440</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.991691</td>\n",
       "      <td>0.926707</td>\n",
       "      <td>0.830746</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.003439</td>\n",
       "      <td>0.962003</td>\n",
       "      <td>0.809621</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.990003</td>\n",
       "      <td>0.915177</td>\n",
       "      <td>0.835582</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.982834</td>\n",
       "      <td>0.975291</td>\n",
       "      <td>0.815475</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.966013</td>\n",
       "      <td>0.895729</td>\n",
       "      <td>0.843981</td>\n",
       "      <td>0.983202</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.970808</td>\n",
       "      <td>0.937748</td>\n",
       "      <td>0.822092</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.952902</td>\n",
       "      <td>0.917925</td>\n",
       "      <td>0.836600</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.959745</td>\n",
       "      <td>0.937104</td>\n",
       "      <td>0.827182</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.951953</td>\n",
       "      <td>0.908588</td>\n",
       "      <td>0.833545</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.943311</td>\n",
       "      <td>0.962126</td>\n",
       "      <td>0.823619</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.944820</td>\n",
       "      <td>0.886919</td>\n",
       "      <td>0.842963</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.930885</td>\n",
       "      <td>0.925723</td>\n",
       "      <td>0.835836</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.915532</td>\n",
       "      <td>0.899836</td>\n",
       "      <td>0.838636</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.907427</td>\n",
       "      <td>0.939050</td>\n",
       "      <td>0.826419</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.898262</td>\n",
       "      <td>0.892906</td>\n",
       "      <td>0.847035</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.904610</td>\n",
       "      <td>0.929464</td>\n",
       "      <td>0.823874</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.901927</td>\n",
       "      <td>0.895526</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.896379</td>\n",
       "      <td>0.883792</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.885624</td>\n",
       "      <td>0.879916</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.901163</td>\n",
       "      <td>0.924473</td>\n",
       "      <td>0.839399</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.883459</td>\n",
       "      <td>0.882869</td>\n",
       "      <td>0.854161</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.874409</td>\n",
       "      <td>0.890418</td>\n",
       "      <td>0.849071</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.866806</td>\n",
       "      <td>0.877330</td>\n",
       "      <td>0.849326</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.876359</td>\n",
       "      <td>0.877673</td>\n",
       "      <td>0.848817</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.869560</td>\n",
       "      <td>0.867235</td>\n",
       "      <td>0.853652</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.856016</td>\n",
       "      <td>0.865260</td>\n",
       "      <td>0.852380</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.851214</td>\n",
       "      <td>0.866879</td>\n",
       "      <td>0.858997</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.868946</td>\n",
       "      <td>0.866100</td>\n",
       "      <td>0.859761</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.856545</td>\n",
       "      <td>0.853927</td>\n",
       "      <td>0.862306</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.851319</td>\n",
       "      <td>0.858997</td>\n",
       "      <td>0.857979</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.843256</td>\n",
       "      <td>0.863415</td>\n",
       "      <td>0.858743</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>01:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.846514</td>\n",
       "      <td>0.855337</td>\n",
       "      <td>0.859761</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.833586</td>\n",
       "      <td>0.848002</td>\n",
       "      <td>0.861288</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.831715</td>\n",
       "      <td>0.855299</td>\n",
       "      <td>0.860270</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.839436</td>\n",
       "      <td>0.848551</td>\n",
       "      <td>0.864342</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.825792</td>\n",
       "      <td>0.846583</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.824599</td>\n",
       "      <td>0.836621</td>\n",
       "      <td>0.871723</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.823247</td>\n",
       "      <td>0.856875</td>\n",
       "      <td>0.862051</td>\n",
       "      <td>0.972512</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.818606</td>\n",
       "      <td>0.844793</td>\n",
       "      <td>0.867905</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.829373</td>\n",
       "      <td>0.843104</td>\n",
       "      <td>0.868414</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.827772</td>\n",
       "      <td>0.847343</td>\n",
       "      <td>0.865106</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.827478</td>\n",
       "      <td>0.835748</td>\n",
       "      <td>0.869687</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.812080</td>\n",
       "      <td>0.834775</td>\n",
       "      <td>0.870705</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.810482</td>\n",
       "      <td>0.843376</td>\n",
       "      <td>0.867396</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.813143</td>\n",
       "      <td>0.828764</td>\n",
       "      <td>0.872232</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.808740</td>\n",
       "      <td>0.830835</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.812579</td>\n",
       "      <td>0.828452</td>\n",
       "      <td>0.872487</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.807382</td>\n",
       "      <td>0.824623</td>\n",
       "      <td>0.874268</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.802656</td>\n",
       "      <td>0.824141</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.806589</td>\n",
       "      <td>0.825350</td>\n",
       "      <td>0.875032</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.809780</td>\n",
       "      <td>0.826118</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.806704</td>\n",
       "      <td>0.824718</td>\n",
       "      <td>0.876304</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.810102</td>\n",
       "      <td>0.824010</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.802804</td>\n",
       "      <td>0.823303</td>\n",
       "      <td>0.878086</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.811189</td>\n",
       "      <td>0.822508</td>\n",
       "      <td>0.876050</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /notebooks/data/imagewoof2\n",
      "Learn path /notebooks/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.081342</td>\n",
       "      <td>2.008984</td>\n",
       "      <td>0.382540</td>\n",
       "      <td>0.824128</td>\n",
       "      <td>00:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.881449</td>\n",
       "      <td>1.634946</td>\n",
       "      <td>0.496055</td>\n",
       "      <td>0.910919</td>\n",
       "      <td>00:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.772940</td>\n",
       "      <td>1.893001</td>\n",
       "      <td>0.388394</td>\n",
       "      <td>0.856961</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.663697</td>\n",
       "      <td>1.398022</td>\n",
       "      <td>0.615933</td>\n",
       "      <td>0.944261</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.572093</td>\n",
       "      <td>1.370641</td>\n",
       "      <td>0.623059</td>\n",
       "      <td>0.948078</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.509336</td>\n",
       "      <td>1.247342</td>\n",
       "      <td>0.685671</td>\n",
       "      <td>0.955714</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.456852</td>\n",
       "      <td>1.258974</td>\n",
       "      <td>0.682107</td>\n",
       "      <td>0.956986</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.391985</td>\n",
       "      <td>1.151992</td>\n",
       "      <td>0.735302</td>\n",
       "      <td>0.960295</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.349106</td>\n",
       "      <td>1.165442</td>\n",
       "      <td>0.730720</td>\n",
       "      <td>0.963349</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.297301</td>\n",
       "      <td>1.075301</td>\n",
       "      <td>0.770171</td>\n",
       "      <td>0.970985</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.269666</td>\n",
       "      <td>1.147965</td>\n",
       "      <td>0.727412</td>\n",
       "      <td>0.966149</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.229458</td>\n",
       "      <td>1.045708</td>\n",
       "      <td>0.780351</td>\n",
       "      <td>0.969712</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.206612</td>\n",
       "      <td>1.050065</td>\n",
       "      <td>0.780097</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.193755</td>\n",
       "      <td>0.985591</td>\n",
       "      <td>0.804276</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.157708</td>\n",
       "      <td>1.072695</td>\n",
       "      <td>0.765844</td>\n",
       "      <td>0.968185</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.150728</td>\n",
       "      <td>0.984473</td>\n",
       "      <td>0.800713</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.122322</td>\n",
       "      <td>1.022696</td>\n",
       "      <td>0.797149</td>\n",
       "      <td>0.973021</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.115481</td>\n",
       "      <td>0.969753</td>\n",
       "      <td>0.806312</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.091607</td>\n",
       "      <td>1.047166</td>\n",
       "      <td>0.782387</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.083569</td>\n",
       "      <td>0.932496</td>\n",
       "      <td>0.829728</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.073083</td>\n",
       "      <td>0.967115</td>\n",
       "      <td>0.816493</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.035627</td>\n",
       "      <td>0.932787</td>\n",
       "      <td>0.825655</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.033329</td>\n",
       "      <td>0.970917</td>\n",
       "      <td>0.803767</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.028585</td>\n",
       "      <td>0.915861</td>\n",
       "      <td>0.828201</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.025620</td>\n",
       "      <td>0.987966</td>\n",
       "      <td>0.800458</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.028341</td>\n",
       "      <td>0.898692</td>\n",
       "      <td>0.834054</td>\n",
       "      <td>0.980657</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.003774</td>\n",
       "      <td>0.962916</td>\n",
       "      <td>0.813184</td>\n",
       "      <td>0.974803</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.985665</td>\n",
       "      <td>0.928279</td>\n",
       "      <td>0.825655</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.976004</td>\n",
       "      <td>0.945130</td>\n",
       "      <td>0.827946</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.971357</td>\n",
       "      <td>0.923539</td>\n",
       "      <td>0.835836</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.966587</td>\n",
       "      <td>0.982666</td>\n",
       "      <td>0.805803</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.945117</td>\n",
       "      <td>0.919980</td>\n",
       "      <td>0.841690</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.950797</td>\n",
       "      <td>0.926997</td>\n",
       "      <td>0.830491</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.944572</td>\n",
       "      <td>0.903141</td>\n",
       "      <td>0.838890</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.937184</td>\n",
       "      <td>0.948772</td>\n",
       "      <td>0.819801</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.944729</td>\n",
       "      <td>0.903903</td>\n",
       "      <td>0.839654</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.924649</td>\n",
       "      <td>0.919735</td>\n",
       "      <td>0.830746</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.926015</td>\n",
       "      <td>0.899656</td>\n",
       "      <td>0.844235</td>\n",
       "      <td>0.975057</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.905790</td>\n",
       "      <td>0.915785</td>\n",
       "      <td>0.837109</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.904521</td>\n",
       "      <td>0.906653</td>\n",
       "      <td>0.842454</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.913745</td>\n",
       "      <td>0.923881</td>\n",
       "      <td>0.836091</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.893094</td>\n",
       "      <td>0.892559</td>\n",
       "      <td>0.844490</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.904264</td>\n",
       "      <td>0.909687</td>\n",
       "      <td>0.842963</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.883390</td>\n",
       "      <td>0.876386</td>\n",
       "      <td>0.850344</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.884714</td>\n",
       "      <td>0.918417</td>\n",
       "      <td>0.838636</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.880066</td>\n",
       "      <td>0.868893</td>\n",
       "      <td>0.853398</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.879958</td>\n",
       "      <td>0.895900</td>\n",
       "      <td>0.845253</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.882591</td>\n",
       "      <td>0.868004</td>\n",
       "      <td>0.858488</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.861035</td>\n",
       "      <td>0.886020</td>\n",
       "      <td>0.849835</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.876313</td>\n",
       "      <td>0.875731</td>\n",
       "      <td>0.858743</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.863303</td>\n",
       "      <td>0.889475</td>\n",
       "      <td>0.852125</td>\n",
       "      <td>0.974294</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.865552</td>\n",
       "      <td>0.868064</td>\n",
       "      <td>0.857216</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.848676</td>\n",
       "      <td>0.887527</td>\n",
       "      <td>0.850853</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.851435</td>\n",
       "      <td>0.859188</td>\n",
       "      <td>0.860270</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.846169</td>\n",
       "      <td>0.870378</td>\n",
       "      <td>0.856961</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.852366</td>\n",
       "      <td>0.865553</td>\n",
       "      <td>0.858234</td>\n",
       "      <td>0.977093</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.845454</td>\n",
       "      <td>0.874864</td>\n",
       "      <td>0.856961</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.847019</td>\n",
       "      <td>0.864079</td>\n",
       "      <td>0.856452</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.825896</td>\n",
       "      <td>0.844588</td>\n",
       "      <td>0.866633</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.827290</td>\n",
       "      <td>0.843232</td>\n",
       "      <td>0.868414</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.820783</td>\n",
       "      <td>0.842548</td>\n",
       "      <td>0.867396</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.830572</td>\n",
       "      <td>0.840566</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.827162</td>\n",
       "      <td>0.843930</td>\n",
       "      <td>0.868414</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.838259</td>\n",
       "      <td>0.849242</td>\n",
       "      <td>0.868923</td>\n",
       "      <td>0.977857</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.828524</td>\n",
       "      <td>0.839131</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.978366</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.828638</td>\n",
       "      <td>0.832316</td>\n",
       "      <td>0.871214</td>\n",
       "      <td>0.977348</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.825059</td>\n",
       "      <td>0.835553</td>\n",
       "      <td>0.870196</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.815881</td>\n",
       "      <td>0.829376</td>\n",
       "      <td>0.871723</td>\n",
       "      <td>0.977602</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.823797</td>\n",
       "      <td>0.826392</td>\n",
       "      <td>0.872741</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.817568</td>\n",
       "      <td>0.816261</td>\n",
       "      <td>0.876559</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.810003</td>\n",
       "      <td>0.820369</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.980911</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.820502</td>\n",
       "      <td>0.825379</td>\n",
       "      <td>0.875286</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.810058</td>\n",
       "      <td>0.821203</td>\n",
       "      <td>0.877577</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.801208</td>\n",
       "      <td>0.823629</td>\n",
       "      <td>0.874268</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.804386</td>\n",
       "      <td>0.822465</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.802714</td>\n",
       "      <td>0.821532</td>\n",
       "      <td>0.873759</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.807972</td>\n",
       "      <td>0.820682</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.979639</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.803859</td>\n",
       "      <td>0.822073</td>\n",
       "      <td>0.875795</td>\n",
       "      <td>0.979384</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.811999</td>\n",
       "      <td>0.819254</td>\n",
       "      <td>0.874777</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.806353</td>\n",
       "      <td>0.820613</td>\n",
       "      <td>0.877068</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>00:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs,mixup=mixup)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### e80 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.876050, 0.876050, 0.877068])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8763893333333334, 0.000479889802165253)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
